Oscilloscopes are electronic devices used to observe and measure electrical signals. They are widely used in engineering, physics, and other fields to measure and analyze signals. Oscilloscopes display waveforms graphically, allowing users to see the shape, frequency, and amplitude of the signal being measured. In this blog post, we’ll highlight their critical characteristics for high-speed digital design and go over some factors to consider when choosing an oscilloscope.

What is an Oscilloscope?

An electronic device called an oscilloscope is used to analyze and display signal waveforms. It consists of a display, signal input channels, and controls for adjusting various settings. The oscilloscope creates a graph by tracking the voltage level of an electrical signal over time. This waveform can then be examined to find any potential issues or signal anomalies.

Oscilloscopes can be used to examine signals in a variety of ways. Oscilloscopes are great for analyzing, validating and debugging electrical systems as they allow to observe the signal change over time in the circuits. They may also be used to find defects in damaged radios, televisions, and other similar devices. Although coaxial cables are used to feed the signal into the probes of a standard oscilloscope, this does not mean that an oscilloscope can only measure electricity. You can use an oscilloscope to measure almost anything by connecting a transducer, which converts one kind of energy into another. For instance, you could study audio signals with an oscilloscope using a microphone (a transducer that converts sound energy into an electrical signal), study temperature changes with a thermocouple (a transducer that converts heat into electricity), or study vibrations with a piezoelectric transducer (which generates electricity when squeezed).

Types of Oscilloscopes

Oscilloscopes can be categorized into different types based on operation mode and the signal processing technologies, but there are two main types as every electronic equipment can be classified: analog and digital.

Analog Oscilloscopes

They are the earliest form of oscilloscopes and use a cathode ray tube (CRT) to display signals in real-time. Despite the advent of newer digital oscilloscopes, they are still used today for certain applications that require a fast response time and a high degree of accuracy.

Analog oscilloscopes (Figure 1) test equipment by directly applying measured signal voltage to its vertical axis, producing a visual representation on the CRT. These oscilloscopes have intensity and focus controls that can be easily adjusted to improve the display’s sharpness.

Figure 1: Tektronix 2445B

Digital Oscilloscopes

The main difference between analog and digital oscilloscopes is that in digital oscilloscopes, the analog signal is captured and converted into a digital signal using an analog to digital converter. In turn digital oscilloscopes classified into four parts:

• Digital storage oscilloscopes (DSO)
• Digital phosphor oscilloscopes (DPO)
• Mixed signal oscilloscopes (MSO)
• Digital sampling oscilloscopes

Digital storage oscilloscopes (DSO) (Figure 2) are the most basic form of digital oscilloscopes, its display typically relies on a raster-type screen rather than the luminous phosphor found in older analog oscilloscopes. It converts the analog signal into a digital format and stores it in its digital memory.

DSOs offer several advantages over analog ones. These include the ability to display a transient quantity over a long period of time, easy production of hard copies, signal processing and computation within the instrument, easy transfer of data to a computer, and the use of inexpensive LCD construction. The development of relatively cheap, accurate, and fast A/D converters has made DSOs available for laboratory and industrial use.

 Figure 2: InfiniiVision 4000 X-Series

Digital phosphor oscilloscope (DPO) (Figure 3) is a newer type of oscilloscope that was first introduced in 1998. Unlike digital storage oscilloscopes (DSOs) which use a serial-processing architecture, the DPO uses a parallel-processing architecture that allows it to deliver unique acquisition and display capabilities for accurately reconstructing a signal and capturing transient events.

After the data is stored in the memory unit, it follows two parallel paths. Firstly, a microprocessor processes the data acquired at each sampling instant according to the settings on the control panel and sends the processed signal to the instrument display unit. Additionally, a snapshot of the input signal is sent directly to the display unit at a rate of 30 images per second. This enhanced processing capability enables the DPO to have a higher waveform capture rate and to detect very fast signal transients that may be missed by DSOs.

Figure 3: Tektronix TDS3000C

Mixed signal oscilloscopes (MSO) (Figure 4) measure both digital and analog signals simultaneously. Obviously, they have more channels than traditional oscilloscopes, making them ideal for testing mixed-signal circuits.

By combining the analog channels of a scope with the logic channels of a logic analyzer, MSOs provide a comprehensive view of a system’s behavior. While it may not be practical to have a 16-channel oscilloscope, a 2 or 4 channel scope combined with a 16-channel logic analyzer function can provide the necessary capabilities to analyze even the most complex systems.

Figure 4: Tektronix 4 Series MSO

Digital sampling oscilloscopes (Figure 5) use a technique called equivalent-time sampling to measure signals. They are ideal for measuring repetitive signals that occur at high frequencies  up to 50 GHz or more, and have low duty cycles. They achieve this by collecting samples from several waveforms and assembling them to build a picture of the waveform.

To optimize for high frequency operation, these oscilloscopes have a different vertical amplifier topology. The signal is sampled prior to amplification to achieve maximum bandwidth. Then a lower frequency amplifier/attenuator combination can be used. However, this reduces the dynamic range of the instrument, limiting the maximum voltage that can be handled to around 3 volts peak to peak.

Figure 5: Keysight N1000A DCA-X

Theory of Operation

• An oscilloscope works by converting electrical signals into a visible waveform that can be analyzed. The signal is first fed into the oscilloscope, where it is amplified and displayed on a cathode ray tube (CRT) or digital display. The waveform displayed on the screen represents the amplitude of the signal over time.
• Waveform can be analyzed based on the amplitude, frequency, phase, and other characteristics. The amplitude of a waveform represents the voltage of the signal, while the frequency represents the number of cycles per second. The phase represents the relative timing of the waveform with respect to a reference signal.
• Signal acquisition involves the process of capturing and sampling the input signal. This can be done using a variety of techniques, such as direct probing, current probes, and voltage probes. The signal is then amplified and digitized for processing.
• Once the signal is acquired and digitized, it can be displayed and analyzed using a range of techniques. Oscilloscopes typically offer features such as triggering, cursors, measurements, and advanced analysis tools to aid in waveform analysis.

Choosing the right Oscilloscope

Choosing an oscilloscope can be a daunting task, with a wide range of specifications and features to consider. We suggest some steps which can help you find the right oscilloscope for your application.

Practical Uses of an Oscilloscope in Various Fields

First of all you need to know where you are going to use your oscilloscope and make a list of your use cases, try to think about the following criteria:

• Are you going to use it in one location or will you need light, easy to carry unit?
• How many input channels do you need? Standard is 2-4 channels that you can observe and compare signal timing, but for debugging a digital system you would likely need 8-16 channels.
• What record lengths do you need? A stable sine-wave signal only needs about 500 points and a basic oscilloscope will store around 2,000 points. But, to troubleshoot timing anomalies in a complex data stream, you might need a record length of up to 1 million points.
• Do you want to be able to connect the unit to a computer? Do you need networking, printing and file-sharing abilities?

Budget and Quality

Oscilloscopes vary in price, depending on the brand, model, features and specifications. You should first determine your budget, as this may vary depending on whether you want to purchase the oscilloscope for long-term or short-term use. Then you have to look for the available options in the specified price range. The opinion of other users and experts should also be taken into account in evaluating the reliability of the quality of the oscilloscope. Generally one should avoid unrealistically cheap low-quality oscilloscopes as those would likely yield inaccurate and many times confusing measurements.

Always review the manual and follow the safety precautions before using your oscilloscope.

Second-hand Oscilloscopes

Second-hand or pre-used oscilloscopes are available at over 90% discount over new ones. Also, the equipment that has already been discontinued by the manufacturers can be found as rental units.

As often times there is no way to test a used oscilloscope in person, there are a few details that can be checked to make sure everything is working as presented. The main detail to look for is in the picture of the instrument with an actual waveform shown on the screen – that means the oscilloscope really does work.
You can also zoom in and try to look through the front-panel settings and make sure matches the waveform shown on the screen.

Key Factors and 5X Rule

• Bandwidth

System bandwidth determines an oscilloscope’s fundamental ability to measure an analog signal – the maximum frequency range that it can accurately measure. So try to select an oscilloscope that has enough bandwidth to accurately capture the highest-frequency content of your signals. The 5X rule says that the bandwidth of the scope with the probe should be at least 5X the maximum signal bandwidth for better than +-2% measurement error. For example scopes with a maximum bandwidth of 100MHz can accurately capture the signals up to 20MHz.

• Sample Rate

The sample rate of an oscilloscope is similar to the frame rate of a movie camera. It determines how much waveform detail the scope can capture. Try to select an oscilloscope that has a maximum specified sample rate that’s fast enough to deliver its specified real-time bandwidth.

The 5X rule says to use a sample rate of at least 5X of your circuit’s highest frequency content, because the faster you sample, the less information you’ll lose. For example entry-level oscilloscopes have a sample-rate of 1-2 GS/s and mid-range have 5-10 GS/s.

• Number of Channels

When selecting a digital oscilloscope, you have to consider the number of channels of acquisition. While more channels are generally better for capturing multiple signals simultaneously, it’s also important to balance this with cost considerations. Ideally, you should choose a scope with enough channels to perform critical time-correlated measurements across multiple waveforms with ease. This ensures that you can accurately analyze complex signals and capture all relevant data for your application.

As mentioned above the standard oscilloscopes have 2-4 channels that you can view and compare signal timing, but for debugging a digital system you may need 8-16 channels.

• Memory Depth

Memory depth refers to the amount of data that the oscilloscope can store. It is typically specified in kpts or Mpts (kilopoints or megapoints) and determines the length of time that the oscilloscope can capture a signal.

Select an oscilloscope with a sufficient acquisition memory to capture your most complex signals with high resolution.

• Triggering

Triggering is used to start or stop data acquisition based on a specific event in the signal. Oscilloscopes offer a range of triggering options, including edge triggering, pulse width triggering, and video triggering. The triggering options of the oscilloscope should be suitable for the intended application. Edge triggering is the most basic triggering option, while more advanced triggering options, such as pulse width and video triggering, may be required for more complex applications.

Select an oscilloscope that offers advanced triggering for analyzing even the most complex waveforms. Better triggering options can help you detect challenging anomalies.

• Display Quality

A high-quality display can help you to accurately analyze your signals, especially for complex or fast-changing signals. Therefore, it’s recommended to select an oscilloscope that provides multiple levels of trace intensity gradation, allowing you to see subtle waveform details and signal anomalies.
This is due to the fact that intensity of a waveform can provide important information about how often a signal repeats. By detecting even subtle signal differences early on, you can avoid costly mistakes and improve your design process.

Conclusion

Oscilloscopes are essential tools for testing and debugging electronic systems, and there is a wide range of oscilloscopes available to suit different applications and budgets. When choosing your design partner, whether it is in house or outsourced,  it is important to consider how the new design will be validated and tested. 

Reach out to us to discuss how we use these tools during testing and validation phase to ensure the success of your next high speed digital design.

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The field of neuroscience and artificial intelligence (AI) have traditionally been studied separately, with little overlap between the two. However, in recent years, there has been growing interest in the potential for cooperation between these two fields. By combining the insights and technologies of neuroscience and AI, we can unlock new possibilities for understanding the brain and creating intelligent machines.

One of the main ways that neuroscience and AI can work together is through the development of brain-inspired AI. By studying the structure and function of the brain, scientists can gain insights into how to create intelligent systems that mimic the brain work. This approach, known as neuromorphic computing, involves building computer systems that are modeled after the neural networks of the brain. These systems can process information in a more efficient and adaptable way, allowing them to perform tasks such as image recognition and natural language processing with greater accuracy.

Artificial Neural Networks (ANNs)

One of the key ways in which AI and neuroscience are driving each other forward is through the development of artificial neural networks (ANNs). ANNs are a type of machine learning algorithm that are modeled after the structure and function of the human brain. These algorithms are designed to learn from data and improve their performance over time, much like the way the human brain learns and adapts.

Neuroscience has played a crucial role in the development of ANNs by providing insights into the structure and function of the brain. For example, neuroscientists have discovered that the brain is made up of a large number of interconnected neurons, which communicate with each other through electrical and chemical signals. This has inspired researchers in the field of AI to develop artificial neural networks that mimic the structure and function of the brain.

In turn, AI has been driving neuroscience forward by providing new tools and techniques for analyzing and understanding the brain. For example, AI algorithms such as deep learning have been used to analyze large datasets of brain imaging data, which has led to new insights into the neural processes underlying cognition and behavior. Additionally, AI-based models have been developed to simulate neural networks and generate predictions about how the brain works, which can be tested and validated through experiments.

Brain Computer Interfaces (BCIs)

Neuroscience has been instrumental in the development of BCIs by providing insights into the neural processes that underlie perception, action, and communication. For example, neuroscientists have discovered that certain patterns of neural activity in the brain correspond to specific movements or actions, which has led to the development of BCIs that can control prosthetic limbs or other devices based on these patterns of activity.

On the other hand, AI has been driving the development of BCIs by providing new algorithms and techniques for analyzing and interpreting neural signals. For example, AI algorithms have been used to classify and decode neural signals, which can be used to control prosthetic limbs or other devices. Additionally, AI-based models have been developed to predict neural signals based on patterns of activity, which can be used to improve the performance of BCIs.

Natural and Human Language Processing

Furthermore, AI and neuroscience are also working together in the field of natural language processing, which involves using computers to understand and generate human language. Natural language processing is an interdisciplinary field, which draws on techniques from AI and computational linguistics, as well as insights from linguistics and cognitive psychology.

In addition to these technical advancements, cooperation between neuroscience and AI can also lead to a better understanding of the brain itself. For example, by using AI techniques to analyze large amounts of brain imaging data, scientists can gain insights into the neural mechanisms underlying various mental disorders, such as autism and schizophrenia. Furthermore, AI can also be used to model the brain’s processes, allowing scientists to test hypotheses and make predictions about the brain’s function in a way that would not be possible with traditional methods.

Of course, there are also ethical concerns to consider when it comes to the intersection of neuroscience and AI. One of the main concerns is the possibility of creating machines that are capable of making decisions and behaving autonomously. While this could lead to the development of intelligent systems that can perform a wide range of tasks, it also raises questions about accountability and the potential for misuse. Therefore, it is important that scientists and policymakers work together to develop guidelines and regulations to ensure that the development of AI is aligned with ethical and moral principles.

In conclusion, the cooperation between neuroscience and AI holds enormous potential for advancing our understanding of the brain and creating intelligent machines. By combining the insights and technologies of these two fields, we can unlock new possibilities for understanding the brain and creating intelligent machines. However, it is important that scientists and policymakers work together to ensure that the development of AI is aligned with ethical and moral principles.

Our team has been expanding our design offerings to optimize AI accelerated hardware solutions with customized software applications – in fact, this blog was partially written and edited utilizing AI tools.  

Interested to know more? Get in touch with us for details.

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The modern-day world is going through a digital transformation process, which triggers technological changes across all economy, commercial, and consumer-focused sectors. These changes include quick penetration of new machines and electronic tools, IoT (Internet of Things) devices, connected software, and other solutions influencing how we interact with technology.

User interfaces of electronic systems and devices change as well. Keyboards, touch panels, switches, graphic interfaces, and buttons are being increasingly replaced by touchless ways of interacting with machines, such as gesture recognition and voice control.

Today we’d like to talk in more detail about different types of touchless interfaces, the technologies that are powering them, and why they are finding more and more applications in other industries, with the increasing frequency being integrated into machines across various commercial and public spaces.

Demand for touchless interfaces post-COVID is on the rise

Touchless interfaces have multiple strengths over other kinds of human-machine interaction (HMI) and human-computer interaction (HCI) technologies. They enable faster access to information and decision-making processes, make interactions with machines and software systems easier, more responsive, safer and more secure.

The relevance of touchless interfaces, already significant, was fueled significantly by the COVID-19 pandemic. In today’s post-pandemic business environment, touchless technologies provide consumers with a safer way to interact with computer systems and machines, without spreading germs and other pathogens.

According to a survey conducted by Ernst & Young, the demand for touchless sensory interfaces has accelerated tremendously since the beginning of the COVID-19 pandemic: 59% of global consumers surveyed said they increasingly lean towards using contactless delivery and cashless payments in the post-COVID world.

Touchless interfaces applications

Organizations in various business fields are now looking to replace old-fashioned human-machine interaction (HMI) and human-computer interaction (HCI) with touchless interfaces.

Here are a few examples of common applications for touchless interfaces across industries:

• Voice assistants and facial recognition in mobile phones,
• Interfaces to interact with ATMs and banking terminals (facial recognition and NFC),
• Automatic translation of sign language,
• Gesture and voice recognition in smart buildings (elevators, entrances, bathrooms, etc.),
• Technologies for hygiene-sensitive areas
• Voice control and gesture recognition in automobiles and other vehicles,
• Gesture recognition in robotic devices and industrial machinery,
• VR (virtual reality) and AR (augmented reality) simulation training systems,
• Contactless sales solutions in retail businesses.

Types of touchless interfaces

Let’s talk in more detail about the most common types of touchless interfaces that are utilized most frequently today.

Voice recognition

Voice recognition is one of the most common types of touchless interfaces implemented today. It allows to simplify interactions with software solutions and machines significantly, but also has certain drawbacks as the voice recognition interface is relatively complicated and can be challenging to operate properly. Voice control is easier to implement for systems that are limited to a fixed set of specific standard commands. Solutions that need to recognize a large number of words would normally require a complex speech recognition software. Voice recognition technology varies by product. Most often products based on this technology allow users to transcribe voice to text, set reminders, search the internet and ask for simple types of content, such as music, weather forecasts or traffic information.

Hand gesture recognition

Hand gestures can be a convenient, relatively easy to implement and highly precise interface solution to control software systems and IoT devices, such as smart home electronics, robots, industrial machines, etc. One of the most common applications for hand gesture recognition today is switching the lights on and off in smart houses. In the automotive industry, hand gesture recognition allows drivers and passengers to interact with the vehicle — typically, to control the infotainment system without touching any buttons or screens.

Face detection and recognition

Face detection and recognition are two distinct types of commonly used touchless interfaces. Naturally, the detection of face, signaling to a computer system about the presence of a person, is considerably easier to implement as it basically just requires an always-on camera with high enough resolution to identify a human face in a continuous video stream. The recognition of specific faces requires a more complex technology behind, normally supported by an AI-powered search and a database of people’s faces stored on a local machine or a remote server.

Body gestures recognition

The recognition of human body gestures is a less common type of touchless interface than the recognition of hands or faces. Still, it also has several applications in specific business fields. Specifically, body gesture recognition can be a solution in training simulation systems for workers in manufacturing and various industrial environments. Fitness and sports training systems are other relevant applications for body gesture recognition interfaces.

Direction of sight, age, face expression, and gender recognition

The most sophisticated types of touchless interfaces allow users to interact with computer systems by means of recognizing specific characteristics such as the line of sight of the person, facial expression (emotions recognition), gender, and age. As an example, such systems have tremendous potential to be used in retail and by other consumer-facing businesses to personalize offers and deliver highly targeted promotional content. For more details about this type of touchless interface, read our previous blog post on human movement technologies. 

Gesture recognition interface technologies

Different algorithms and models powering touchless interfaces exist. Let’s review the most common approaches utilized in gesture recognition as universal types of touchless interfaces used today.

Any gesture recognition system is built on two basic processes: the acquisition of input data and its recognition. The acquisition is converting physical human gestures into digital data. It is typically performed using all kinds of sensor-based devices, such as cameras, gesture-based controllers, motion detectors, wired gloves, etc.

The interpretation of acquired data is typically implemented with a number of different algorithms and approaches. Here are the most common ones:

3D model-based algorithms

3D model-based algorithms rely on using volumetric or skeletal models created from complex three-dimensional surfaces. In some cases, volumetric and skeletal models are used in combination with each other.

Electromyography-based algorithms

Electromyography (EMG) is a technology that allows the recording of electrical signals produced by muscle movements inside the human body. The EMG data is typically recorded either by sophisticated cameras able to detect muscle movement or by electrodes placed directly on the skin.

Skeletal-based algorithms

Skeletal-based models are a simpler and cheaper alternative to 3D model-based algorithms. This approach creates a virtual skeletal representation of the person’s body by digitally mapping all the main segments of the skeleton and analyzing the positioning of the body parts based on this model.

Appearance-based models

Appearance-based models rely on creating a representation of the body or, more frequently, body parts based on two-dimensional templates of the human body parts. Such models are mostly used for hand gesture detection and recognition, so they typically require templates of a human hand with a selection of the most basic hand gestures.

Summary

The demand for touchless interfaces in the modern world is growing quickly, fueled by the post-COVID precautions and the need to integrate new ways of interacting with computers, machines, robots, and software systems across markets and industries, from consumer electronics and industrial automation to construction, healthcare, education, and entertainment industry. 

The design and integration of a touchless interface requires a common effort of a high-profile team of embedded systems engineers, ergonomic specialists, software developers, and other experts.

The Tauro Technologies team of electronic engineers and designers has a proven track record of successfully designing custom hardware for various kinds of embedded systems and IoT products in multiple technology fields. Drawing on the specific needs of our clients, we select and apply various engineering methods to electronic product development and manufacturing in order to achieve the desired result. Utilizing our in-house expertise in assembly and debugging of embedded systems and touchless interfaces, we are able to build and evaluate your prototypes before high-volume manufacturing, rapidly and cost-efficiently.

Interested to know more? Get in touch with us for details.

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We are living in the age of profound and pervasive technological transformations that are rapidly spreading across both enterprise and consumer product markets. The Internet of Things (IoT) and electric vehicles (EVs) are two examples of innovative cutting edge solutions marking the dawn of a new era of global tech development. Utilized in combination with each other, IoT and EVs can deliver a multitude of benefits, helping to minimize air pollution, optimize EV performance, reduce energy consumption, and achieve many other improvements that are part of the smart city of the future concept.

IoT and EV industries to show rapid growth over the course of the 2020s

Despite a significant drop in the number of new EV registrations due to the COVID-19 pandemic and economic turbulence (registration of all types of new electric vehicles during 2021 financial year dropped by 20% compared to 2020), the global electric vehicle market continues to evolve and will achieve a tremendous growth over the course of the current decade. Several recent studies use slightly different approaches to quantify the anticipated growth ahead but all point to an 18-26% CAGR:

•  According to a recent study by Allied Market Research, the EV market was valued at $163.01 billion in 2020, and is projected to reach $1105 billion by 2030, registering a CAGR of 18.2% from 2021 to 2030.

• Based on this report by AMR, it was valued at $740.47 billion in 2020, and is projected to reach $4,421.62 billion by 2030, growing at a CAGR of 19.67% from 2021 to 2030. 
• A study by Fortune Business Insights features lower market size estimations but follows the same growth trajectory, estimating it to grow from $478.36 billion in 2022 to $2,465.26 billion by 2029, at a CAGR of 26.4% during the forecast period.

• According to a recent market research study by Custom Market Insights, the EV market was valued at USD 170 Billion in 2021, and USD 205.4 billion in 2022 and is projected to reach $1105 billion by 2030, registering a CAGR of 23% from 2022 to 2030.

IoT applications in the EV industry 

Such a rapid growth of the global fleet of electric vehicles creates a number of needs and challenges, most of which can be solved with the implementation of IoT solutions:

• The need to implement and maintain an extensive network of charging stations for EVs.
• Battery management solutions to monitor and control the EV battery charging and discharging cycles and other processes.
• Vehicle speed, mileage, and acceleration monitoring.
• Safety, driving errors and fault alert tracking systems.
• Protection against theft.
• Preventive maintenance for vehicle components.
• Crash detection.

Benefits of IoT Integration for EV Management and Monitoring 

Let’s review the IoT solutions and technologies that can be implemented as part of electric vehicles and supporting equipment (charging stations, maintenance and repair stations) for the benefit of EV manufacturers, drivers, cities/communities and the environment.  

EV Charging Stations

The integration of IoT components into the EV charging stations allows to overcome a number of challenges and achieve multiple goals.

Here are a few examples:

• The integration of IoT-based telematics technology into EVs and EV charging stations enables collection of real-time data and automatic generation of various reports.
• IoT-powered charging applications allow drivers to easily locate nearby stations and schedule a time slot for charging.
• Implementation of IoT sensors would allow charging station operators to track their status and energy consumption remotely through a centralized system, quickly reacting when the station is disconnected or damaged.
• The collection of data through IoT sensors enables drivers to access real-time notifications with charging time estimations, information about cable position, weather conditions, etc.
• The stream of real-time data from charging stations and connected electric vehicles allows station operators to send targeted ads and promo materials to the owners of EVs.

EV Battery Management Systems

Battery management systems (BMSs) are another crucial element of the EV infrastructure that can be significantly improved and optimized with IoT components. BMSs are used to monitor and control various processes inside the EV battery, including charging, energy consumption and distribution of power from the battery across the vehicle equipment.

IoT circuits integrated inside an EV battery management system can serve a number of important processes:

• Real-time monitoring of all key battery parameters such as temperature, current, voltage, charge, etc.
• Continuous evaluation of the State of Charge (SoC) and the State of Health (SoH) of the battery with instant notifications when changing, maintenance or repair is required.
• Location tracking to prevent theft or loss of the battery.
• Exchange of data collected by integrated IoT sensors with battery swapping stations for predictive analytics, as well as with car leasing companies.

EV Maintenance and Fault Alert

IoT-based fault alert systems, retrieving and automatically analyzing data collected by sensors integrated throughout different parts of the electric vehicle, are able to quickly identify and in many cases predict technical glitches in the EV, notifying both the car owner and connected EV service centers.

The IoT data collected by EV sensors can be utilized for the following applications:

• Monitoring the wear and tear of EV parts.
• Maintenance of the car tires.
• Tracking of lubrication cycle of the engine and other parts.
• Monitoring the temperature and moisture conditions within the eV components.
• Scheduling preventive maintenance based on mileage traveled.

Safety Control and Detection of Driving Errors

Various safety controls, such as monitoring of driver’s performance, prevention of driving errors and protection against crashes, is one more application of IoT technologies in electric vehicles.

Using IoT sensors, EV manufacturers are able to establish the following safety features:

• Real-time monitoring of vehicle speed to make sure it doesn’t surpass the speed limit.
• Automated collection and analysis of the driver behavior information such as average speed, acceleration, etc. This data can be then used to provide the EV driver with tips and notifications, as well as to share it with insurance companies.
• Tracking of vehicle location and status in real time and sending notifications to tow truck service and police in the case of emergency.
• Sending the data collected by the in-built sensors to the EV manufacturer for the analysis and improvement of the car component quality and safety of the vehicle. 
• Detection of crashes and/or EV fall down with built-in IoT accelerometers.

Protection against theft

Finally, the IoT technologies can be a great solution to protect electric vehicles against theft. With real-time tracking and geo-fencing, it is possible to prevent not just the theft of the car itself but also its separate parts by integrating IoT sensors in equipment such as batteries, which are the most expensive parts of the EVs typically comprising around 40% of the total cost of an EV. Alarms and notifications can be sent to the vehicle owner and local authorities in the case of unauthorized access to the car or removal of its parts.

Summary

As you can see, IoT technologies have a tremendous potential to empower the already rapidly growing electric vehicle industry, improving virtually all aspects of EV manufacturing, monitoring and management. The integration of IoT can significantly boost the performance of charging stations, prolong the lifespan of EV equipment, protect drivers from accidents, ensure vehicle security and reduce the energy consumption.

That’s why we believe it is safe to predict that the demand for embedded systems in the EV industry will be on the rise at least for the next ten years or so. In this increasingly complex and competitive business environment, the importance of professional approach to IoT and embedded systems design starts to play an even more important role.

The Tauro Technologies’ team of electronics engineers and firmware designers has a proven track record of successfully designing custom hardware for various kinds of embedded systems and IoT products in multiple technology fields including control systems, battery management, and IoT devices. Drawing on the specific needs of our clients, we select and apply various engineering methods to electronic product development and manufacturing in order to achieve the desired result. Utilizing our in-house assembly and debug expertise, we are able to build and evaluate your prototypes before high-volume manufacturing, rapidly and cost-efficiently.

Interested to know more? Get in touch with us for details.

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The use of data analytics tools for online shopping is widespread and if we judge by the hype, we imagine it is everywhere in retail stores as well. Camera systems installed in most retail locations generate vast streams of data every second that are impossible to monitor manually in real-time.

Whether we like it or not, our every move when we engage with retail sites on the web is tracked, analyzed and monitored using tools like click management, heat mapping, customer purchase history, preferences & demographics, to deliver a hyper targeted shopping experience. Stores can easily attempt to close abandoned online baskets, upsell and cross sell other products to the site visitors. ROI(Return On Investment) is easily measured by customer engagement and incremental purchases.

Historically, it is more difficult to track and analyze consumer behaviors in brick-and-mortar stores and provide them with a personalized experience without the direct human interaction. However, advances in AI are bringing promise to enable retailers to better analyze and manage their customer interactions.

Utilizing AI in brick and mortar:

Being efficient with advertising is the goal of every marketer and the retail industry is greater than 15% of the entire digital ad spend annually. It is challenging to quantify the results – managing data is the key and is both an art and a science. For example, a big box lumber and hardware chain sends out a flier with a seasonal promotion. Currently they are able to measure that traffic was up 5% and sales were up 10% compared to the previous week. But was the campaign really effective? What if the example big box marketing department could utilize the video feeds in their store and have their AI answer some of the following questions:

• Percentage of people that specifically visited and purchased the items on the promotion.
• Effectiveness of complementary product placement
• Customer experience and path through the store
  • Which departments did they visit?
  • Were they helped by staff?
  • Did they purchase additional products after staff interaction?
  • Length of time a checkout
  • Percentage of people that did not purchase an item
  • Average length of the store visits compared to the previous weeks.

While it is impossible for humans to watch and track hundreds of shoppers simultaneously, it is easily accomplished with object detection and tracking in many of today’s edge AI platforms.  Where it was previously expensive and resource prohibitive, the retailer can also do valuable A/B testing across different stores or within a store and make immediate changes to increase profits, inventory levels, revenues and efficiency.  Examples of these tests may include:

• Staffing levels & training.
• Product placement – retailer marketers invest massive resources to optimize. 

How does AI review patterns of human movement:

Pattern recognition is a complex process of analyzing input data, extracting patterns, comparing them with certain standards, and using the results to guide the future actions of the system. Pattern recognition involves recognition of surrounding objects in an artificial manner achieved through machine learning and pattern recognition algorithms. In other words, it is the process of identifying the trends in the given pattern. In the Machine Learning(ML) space, pattern recognition shows the use of robust algorithms in order to identify the regularities in the given set of data.

The following image (Fig 1) shows how data is used for training and testing:

The training set contains images or data used for training or building the model. Training rules are used to provide the criteria for output decisions.  Training algorithms are used to match a given input data with a corresponding output decision. The algorithms and rules are then applied to facilitate training. The system uses the information collected from the data to generate results.

The testing set is used to validate the accuracy of the system. The testing data is used to check whether the accurate output is obtained after the system has been trained. This data represents approximately 20% of the entire data in the pattern recognition system.

There are three basic approaches that pattern recognition algorithms utilize:

• Statistical. This approach is based on statistical decision theory. Pattern recognizer extracts quantitative features from the data along with the multiple samples and compares those features. However, it does not touch upon how those features are related to each other.
• Structural (a.k.a. syntactic). This approach is closer to how human perception works. It extracts morphological features from one data sample and checks how those are connected and related.
• Neural. In this approach, artificial neural networks are utilized. Compared to the ones mentioned above, it allows more flexibility in learning and is the closest to natural intelligence.

Every machine learning-based pattern recognition algorithm includes the following steps:

• Input of data. Large amounts of data enter the system through different sensors.
• Preprocessing or segmentation. At this stage, the system groups the input data to prepare the sets for future analysis.
• Feature selection (extraction). The system searches for and determines the distinguishing traits of the prepared sets of data.
• Classification. Based on the features detected in the previous step, data is assigned a class (or cluster), or predicted values are calculated (in the case of regression algorithms).
• Post-processing. According to the outcome of the recognition, the system performs future actions.

Alongside machine learning, deep learning is also implemented in training pattern recognizers when it comes to neural networks.

Human activity recognition consists of four stages (Fig 2) including (1) capturing of signal activity, (2) data pre-processing, (3) AI-based activity recognition, and (4) the user interface for the management of HAR (Human Activity Recognition). Each stage can be implemented using several techniques bringing the HAR system to have multiple choices. Thus, the choice of the application domain, the type of data acquisition device, and the processing of artificial intelligence (AI) algorithms for activity detection makes the choices even more challenging. 

Many retailers have already installed camera systems for security purposes. A solutions integrator can install a computer system with an AI accelerator to provide customer heat mapping, traffic patterns, and surveillance. 

• The systems auto tag, recognize gender, size of group (single or family shoppers) etc.
• Recognize and provide real-time computation and output of shopper data analysis that can be actioned immediately by the retailer.   

When it comes to tracking human movement, the AI is trained to use the video feed to recognize parts of the body like the head, arms, and legs as shown in Fig 3. Once these parts are recognized, the pose of the person is then analyzed, such as if they are standing, sitting, walking etc. and how they move as time progresses with additional frames. The algorithms can be refined to track the person through the store, analyze their movements and provide alerts if there is a security instance.

Machine learning-based pattern recognition systems are also being applied to extract greater value from existing data. Machines can look at data to find insights, patterns and groupings and use the power of AI systems to find patterns and anomalies humans aren’t always able to see. This has broad applicability to both back-office and front-office operations and systems. Whereas, before, data visualization was the primary way in which users could extract value from large data sets, machine learning is now being used to find the groupings, clusters and outliers that might indicate some deeper connection or insight.

Further benefits of AI in Brick and Mortar Retail

In addition to providing security and data analytics for marketing purposes, AI in retail provides real-time data that can be used to improve efficiencies. For example:  

• Consumer purchasing decisions often revolve around the change of seasons, holidays, and also weather. With real-time analytics, it is easier for the retailer to adjust product placement and offerings.
• Large chains are known to change their prices 1000s of times per week to maximize their revenue, profitability, and to manage inventories.  
• Tracking shopper behavior and engagement in a retail setting including staff interactions enables better brand engagement and optimization.
• Although most retail purchases still take place in stores, many sales are driven by online presence. AI analytics can provide further insight into consumer brand awareness and product choice.

Tauro Technologies is working with several integrators on the crucial building blocks that will enable the brick and mortar business to further transform in the coming decade. Reach out to us if you are interested in learning more.

The post Leveraging Artificial Intelligence for Brick-and-Mortar Stores appeared first on Tauro Technologies.

An Artificial Intelligence (AI) accelerator accelerates artificial intelligence applications such as artificial neural networks and machine learning. In the last decade, graphics processing units (GPUs) have seen increasing adoption for these applications since they efficiently perform image processing and mathematical neural network calculations. Fortunately for AI development, GPU manufacturers such as NVIDIA are making GPUs that greatly enhance AI performance, opening the door to many new computing products at the edge.  NVIDIA products have led the market with innovation and are widely used in AI computing. However, other specialty GPU providers offer enhanced performance on portions of AI computing, such as AI inferencing. 

What is a GPU?

A modern Graphical Processing Unit or GPU is similar to a CPU but makes use of parallel processing and is able to handle many processes and threads at the same time. Due to its parallel processing, a GPU is normally used for graphics processing and rendering.

GPUs have been around since the 1970s, primarily used for arcade games such as Sea Wolf and Space Invaders. Graphics cards were not commonly used in PCs until the mid-1980s, when the NEC μPD7220A became the first processor with a Large Scale Integration circuit chip, making it the most popular GPU. The next significant innovation was in the 1990s with S3 Graphics making the S3 86C911. This new GPU used 2D acceleration to gain a massive performance increase compared to its competitors. Today, a GPU is one of the most crucial hardware components of computer architecture.

Initially, the purpose of a video card was to take a stream of binary data from the central processor and render images to display. But modern graphics processing units are engaged in the most complex calculations, like big data research, machine learning, and AI.

While AI has existed since the 1990s in inference and training, AI accelerators did not enter the market until 10 years ago, when the workloads of AI processes became more intensive. Since 2010, AI accelerators such as field-programmable gate arrays (FGPA) and customized application-specific integrated circuits, (ASIC) are being replaced by commercial GPUs, which offer faster time to market and lower development costs. 

The GPU Market

Currently, the GPU industry is dominated by three large companies: NVIDIA, Intel, and AMD. These companies have been around for the longest, with NVIDIA pioneering the discrete GPU market since 2000 and putting them into a firm leadership position.  New entrants into the GPU market are picking a product niche and developing solutions that are efficient and designed to fill the appetite for higher performance with lower costs per watt of power. Up to 90% of the power consumed by an image processing application is accessing the RAM. Today, there are new entrants with differing approaches to building more efficient GPUs, including Mythic.AI, UntetherAI, Hailo, Blaize, etc., for edge AI computing.  These companies focus on avoiding data transfer between computing and memory to drive efficiency.  

GPU vs CPU

In the past, CPUs were used to process information for artificial intelligence. However, as GPUs have become more powerful over time, their ability to process more parallel information faster has made them a much better solution for AI. 

Integrating a GPU into an application:

GPUs are typically shipped as modules to be easily implemented into various applications. Chip-down GPU designs are intensive hardware and software projects; also, GPU vendors historically will only support Tier 1 customers and projects and encourage the rest of the applications to design a carrier that integrates their modules. 

Here are some common GPU module form factors:

PCIe

• This is the first and still most common form factor for GPU modules and is easily integrated with common PC motherboards with up to x16 Gen5 PCIe connections. However, the disadvantage is that they are large and bulky for many edge AI applications and typically require forced air to cool them.

MXM (Mobile Express Module)

• As the name indicates, the MXM form factor was developed to offer graphics processing module capabilities to smaller mobile computers such as laptops. It is also commonly used in edge SFF computing applications in markets such as military, medical, and transportation. The modules can be air cooled or conduction cooled.

M.2

• The M.2 standard replaces the mSATA standard and offers size and speed advantages for storage.  Gen4 PCIe x4 connections to the processor allow it to be also widely used for other computing functions, including GPS, LTE, IO, and smaller GPUs by vendors such as Hailo.  

E1.S EDSFF (Enterprise and Datacenter Small Form Factor)

• E1.S is the choice of next-generation storage modules.  It offers greater density and performance than the M.2 and other earlier form factors.

• Although it is being deployed in the datacenter server industry, it has not yet replaced M.2 as a standard in SFF Edge Computing.  
• Blaize is an example of a GPU being deployed in the ES.1 SFF, enabling up to 512 TOPS in a 1U server and 16-64 TOPS in an SFF computer.

SOM (System on Modules)

• System on Modules has been made popular for industrial edge computing by NVIDIA in products such as JETSON.
• Embedded ARM processors in the SOM enable a solutions provider to build a smaller, low-cost edge AI or graphics processing computer without needing a separate embedded CPU.

Chip-Down

• With many edge platforms, it is the most cost-effective to design a solution with a ‘chip-down’ GPU and a ‘chip-down’ CPU.  Tauro Technologies has designed these systems for several of the GPU vendors mentioned above.

GPU integration into a carrier board:

System design takes many factors into consideration, including cooling, power, I/O, storage, processing, etc., and each system requirement is different. Typically, GPU modules that are not chip-down are integrated into a main board or carrier board that has been customized to meet the application requirements.  

There are 2 main types of processors integrated into carrier boards:

• Intel x86: Due to the engineering complexity of Intel chip-down designs, many edge AI applications choose to use COMe or COM-HPC client-based modules to simplify their carrier design projects. Although the material cost of the module is higher than a chip-down solution, unless the product reaches modest volumes, it is more cost-effective to design with an x86-based module. 

• ARM-based processors: ARM processors are typically lower cost and take less power, making them ideal for many computer vision products when paired with a GPU.  Since there are few modules available based on open standards, most ARM-based products are custom designed with a chip-down processor such as NXP Cortex or Layerscape.  

Summary:

The advent of AI is opening the door for application-specific GPUs that are finely tuned to the objectives of the project. Tauro Technologies has broad experience implementing edge computers optimized for the application. If you wish to discuss a customized high-volume platform or a system which takes advantage of commercially available hardware and tools, reach out to us.

The post Use of GPUs in Edge AI Computing: appeared first on Tauro Technologies.

Serial communications protocols are vital to embedded systems.  While UART, I²C, and SPI have been used for short-distance device communication for decades, the benefits are not entirely apparent.

In order to connect peripherals to a computer, one of the following protocols is typically employed: a Universal Asynchronous Receiver Transmitter, (UART) Inter-Integrated Circuit, (I²C) or Serial Peripheral Interface (SPI). This blog will compare and contrast the features of each protocol and help you determine the best fit for your application.   

UART

Universal Asynchronous Receiver Transmitter (UART) is an asynchronous serial communication device with its roots dating back to the telegraph. There is no clock signal to synchronize or validate the data transmitted from the transmitter and received by the receiver (Asynchronous Serial Communication). It sends 1 bit at a time from least significant to most significant and uses start and stop bits to enable precise clocking. During packet transmission, UART uses what is called a parity bit, to enable checking if the information has changed during transmission. 

In addition, data transmission between devices can be in simplex, half-duplex, or full duplex modes.

Data is transmitted at baud rate measured in bits per second – some of the standard baud rates are 4800 bps, 9600 bps, 19200 bps, 115200 bps etc. Out of these, 9600 bps baud rate is the most commonly used.

UARTs must be set for the same bit speed, character length, parity, and stop bits for proper operation on the transmit and receive side. If the receiving UART detects mismatched settings a flag is sent in the host system memory to indicate a failure. 

The data in UART serial communication is organized into blocks called Packets or Frames. The structure of a typical UART data packet or the standard framing of the data is shown in the following table:

Advantages:

• Management is straightforward through hardware. It is utilized by standard protocols including RS-232/485/422.
• Long-distance up to 1km for RS-422/485 buses.
• Requires only two wires for full-duplex data transmission (other than power lines).
• Parity bit ensures basic error checking is integrated into the data packet frame.
• No need for clock or any other timing signal.

Disadvantages:

• Communication is only between two devices where the baud rate, data bit count, parity bit, and stop bit count need to be identical.
• Typically the size of the data frame is limited to only 9 bits (8 data bits, no parity bit and one stop bit).
• Overrun errors if the buffer space is insufficient.
• Size of data in the frame is limited.

I²C

Unlike UART, an Inter-Integrated Circuit is a synchronous serial communication interface and utilizes the system clock. It means that data bits are transferred one by one at regular intervals of time set by a SCL clock line. It is used primarily for short distance, intra-board communication between low speed controllers and processors and is ideal for applications that link up to many components on a bus. Although I²C is typically implemented with a single master and multiple slaves on the bus, it can also be implemented with multiple masters.  Each slave device has a unique address and I²C enables the master to send and request data from a particular slave device utilizing a start bit to the slave address.  

I2C only uses two wires to transmit data between devices:

• SDA (Serial Data) – The line for the master and slave to send and receive data.
• SCL (Serial Clock) – The line that carries the clock signal (common clock signal between multiple masters and multiple slaves).

The structure of a typical I²C Data Packet or the standard framing of the data is shown in the following table (Note: The bold signals are sent by slave and the other signals by master):

Advantages:

• Addressing function enables multiple masters and slaves.
• Control a network of devices with only 2 I/O pins.
• Simple mechanism for validation of data transfer.
• I²C networks are easy to scale. New devices can simply be connected to the two common I²C bus lines.
• No need for prior agreement on data transfer rate as in UART communication.

Disadvantages:

• Slower speed (up to 100 kbit/s in standard mode, 400 kbit/s in fast mode)
• Half-duplex interface.
• Only one slave can be addressed at a time.

SPI

The Serial Peripheral Interface (SPI) is also a synchronous serial communication device which is used primarily for short distance communication. The main difference between SPI and I²C is that SPI uses a full-duplex communication with master-slave topology. Similar to I²C , SPI can be used to access multiple slave devices.

At the beginning of communication, the bus master configures the clock (typically 50 MHz) and sends data to the slave.  During a single SPI clock cycle, a full duplex of data transmission is completed.  Unlike UART, there are no start and stop bits – this enables continuous data transmission and the communication achieves higher speeds than I²C and UART.

The maximum data rate limit is not specified in the SPI interface. Standard data rates include 10 Mbps transfer rate with some devices reaching 100Mbps transfer rate.

The SPI bus consists of 4 signals below:

• Master – Out / Slave – In (MOSI)
• Master – In / Slave – Out (MISO)
• Serial Clock (SCLK)
• Chip Select (CS) or Slave Select (SS)

Depending on the values of Clock Polarity (CPOL) and Clock Phase (CPHA), there are 4 modes of operation of SPI:

• Mode 0 is active when Clock Polarity is LOW and Clock Phase is LOW (CPOL = 0 and CPHA = 0). Data sampled on rising edge and shifted out on the falling edge.
• Mode 1 is active when Clock Polarity is LOW and Clock Phase is HIGH (CPOL = 0 and CPHA = 1). Data sampled on the falling edge and shifted out on the rising edge.
• Mode 2 is active when Clock Polarity is HIGH and Clock Phase is LOW (CPOL = 1 and CPHA = 0). Data sampled on the falling edge and shifted out on the rising edge.
• Mode 3 is active when Clock Polarity is HIGH and Clock Phase is HIGH (CPOL = 1 and CPHA = 1). Data sampled on the falling edge and shifted out on the rising edge.

The structure of a typical SPI data packet or the standard framing of the data is shown in the following image:

Advantages:

• Full-duplex is default for the SPI protocol.
• Slaves do not require a unique address.
• Not limited to 8-bit word size.
• Real-estate savings on embedded boards.
• High data transfer speed.
• No need for individual addresses for slaves as CS or SS chip-select lines are used.
• Only one master device is supported, removing the possibility of conflicts.
• SPI uses less power than I²C.

Disadvantages:

• No protocol-level error checking function and no hardware slave acknowledgement.
• Short distances (up to 10m).
• Each additional slave requires an additional dedicated pin on the master for CS or SS.
• There is no acknowledgement mechanism and hence there is no confirmation of data receipt.
• Slowest device determines transfer speed.

Summary

In general, you can use UART if you are looking for a simple connection between 2 devices, I²C if you are connecting several devices on the same bus, and SPI becomes the ideal choice if you require a faster interface.  Whether you need UART’s tried and true operation, or want to utilize the expansion offered by I²C or the high speed of SPI, Tauro Technologies can implement a system using the most appropriate interface for your project.  

Interested to know more? Get in touch with us for details

The post Serial Protocols & Their Uses: I2C, UART, SPI appeared first on Tauro Technologies.

Most modern laptop or desktop computers have their peripheral devices (storage, media, or cameras) connected to the main processor through a peripheral bus such as PCIe or USB.  Windows or Linux operating systems running on the computer can discover the connected peripherals through a process called enumeration or ‘plug and play’.  This provides information about device type, manufacturer and device configuration thus enabling the OS to load the appropriate drivers for the device and making device operational.  

However, in embedded systems, this is not the case as many peripherals are connected to the main processor using busses such as I2C, SPI, and UART which do not support enumeration.  

To enable the system to recognize the peripheral devices in an embedded system, developers use a Linux Device Tree which is used to provide the hardware description for the operating system.  Prior to using device tree, developers would compile the hardware description into the linux kernel and modify the kernel for each change in platform or peripheral device.  

What is a Device Tree?

A Device Tree is a tree data structure with nodes that describe the devices in a system. Each node has property/value pairs that describe the characteristics of the device being represented. Each node has exactly one parent except for the root node, which has no parent.

In the Device Tree each node is named according to the following convention: <name>[@<unit-address>].

• <name> is a simple ASCII string and can be up to 31 characters in length. In general, nodes are named according to device type it represents. A node for a 3com Ethernet adapter would use the name ethernet, not 3com509.
• <unit-address> component of the name is specific to the bus type on which the node resides. The <unit-address> must match the first address specified in the reg property of the node. If the node has no reg property, the @<unit-address> must be omitted and the <name> alone differentiates the node from other nodes under the same level in the tree hierarchy. In case <name> is used without @<unit-address>, the <name> shall be unique within the same level in the tree hierarchy.

Figure 1 represents simple tree:

• The nodes with the name cpu are distinguished by their unit-address values of 0 and 1.
• The nodes with the name ethernet are distinguished by their unit-address values of fe002000 and fe003000.

A node in the Device Tree can be uniquely identified by specifying the full path from the root node, through all descendant nodes, to the desired node.

The convention for specifying a device path is: /node-name-1/node-name-2/node-name-N.

Each node in the device tree has properties that describe the characteristics of the node. Properties consist of a name and a value. A property value is an array of zero or more bytes that containz information associated with the property.

With the Linux Device Tree, the developers can create a single linux kernel image specific to a processor architecture and create multiple device tree images specific to a platform or a product.  This makes it much easier to support and update the peripheral changes in various products and platforms that one desires to support.

In x86 based platforms, ACPI is commonly used to describe the hardware peripherals and it can be used with or without the Linux Device Tree.  However, in non-x86 platforms such as ARM based systems, Linux Device Tree is becoming the common method of enumerating hardware peripherals.

How is Device Tree data managed?

Data from the Linux Device Tree can be shown in multiple different ways. Usually, the device tree data is in a format that is readable to humans in .dts or .dtsi source files. The Linux kernel pre-processes the dts files before passing them to the device tree compiler.  The source code of the device tree is compiled into a .dtb blob file in a binary format, this format is generally called a Flattened Device Tree (FDT). With this data, the Linux OS is able to find and identify devices in the system. The raw form of the FDT is accessed by the OS during very early stages of the system booting up, but is then further expanded into a kernel data form called the Expanded Device Tree (EDT) so that it can be accessed later during and after the booting up of the system more efficiently. 

As of today, device tree support is enabled in linux kernel for Microblaze, Sparc, ARM, PowerPC and x86 architectures. To unify the handling of description of platforms in various kernel architectures, there is interest to extend device tree support to other platforms.

Device Tree Advantages and Disadvantages:

In summary, here are the advantages and disadvantages of Linux Device Tree.

Advantages of the Linux Device Tree include:

1. It makes changing the configuration of parts of the system very simple without having to recompile any of the linux kernel source code.
2. Easier support for new/additional hardware.
3. Can reuse .dts files that are already existing within the system and can override old functionality. 
4. It makes it easier to understand the descriptions of hardware peripherals.

However, disadvantages include:

1. Creation of .dts files require extensive knowledge of hardware and thus may not be easy to create.
2. Figuring out all the necessary syntax to match the intended system function may be difficult even if the user knows all the bus and device details.

We trust this generic insight into Linux Device Trees is helpful for your system development. Tauro Technologies implements and customizes device trees as part of Board Support Package (BSP) development and board bring-up. 

Interested to know more? Get in touch with us for details

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For quite a while, since the rise of smartphones in the late 2000s, the computer processors market has been dominated by ARM central processing units (CPUs) based on the reduced instruction set computer (RISC) architecture. Recently, however, a strong competitor has emerged with a considerably different approach towards the CPU architecture in microprocessors, mobile systems and microcontrollers. The name of this potential ARM killer is RISC-V (pronounced as “risk-five”). 

Over the last couple years, the debate regarding the competition between ARM and RISC-V has been getting more and more vibrant. 

Will RISC-V ultimately replace ARM as the top CPU specification or will both technologies coexist? Let’s take a closer look at these two computer processor architectures, their technical specifications and how they are different from each other. 

What is ARM? 

ARM (originally known as Acorn RISC Machine, ARM stands for Advanced RISC Machines) is a family of RISC instruction set architectures for computer processors, available for various computing devices and environments. 

The ARM CPU architecture is developed by the Arm Ltd company, which licenses the architectures to other companies, allowing them to design their own products that incorporate different components, including interfaces and memory. 

There have been a number of generations of ARM architecture. The original version, ARM1, was introduced in 1985, almost 40 years ago. First application for ARM processors was as an additional second processor for the BBC Micro, providing support to speed up the simulation software. ARM1 used 32-bit internal structure but also had 26-bit address space, limiting it to 64 MB of main memory. This limitation was removed in ARM 3.

ARM 8-A, released in 2011, received the support for 64-bit address space and 64-bit arithmetic. 

ARM processors quickly gained popularity due to their low power consumption, lower costs compared to available alternatives, and minimal heat generation. 

Even though ARM CPUs were widely used since the initial release of this architecture, they really came to power in the late 2000s, upon the release of the first smartphones. Being the best CPU choice for portable devices due to light weight and low power consumption, ARM processors are preferred by the manufacturers of smartphones, tablets and laptops. For the same reasons, ARMs are also widely used in embedded systems. 

According to the official data, more than 200 bln ARM chips have been produced around the world as of 2021. 

What is RISC? 

Since we already mentioned the RISC a number of times, a few words about it need to be said as well. 

RISC is a technology designed to simplify the individual instructions provided to the computer to perform certain tasks. The difference between RISC and CISC (a complex instruction set computer) is that RISC architecture typically requires more instructions provided to a computer in order to complete tasks as individual instructions in RISC are written in simpler code. 

One of the key concepts of RISC computers is that every instruction performs only one function during single CPU cycle. 

What is RISC-V?

RISC-V is basically the fifth generation of the RISC architecture, provided as an open standard instruction set architecture (ISA) based on the RISC standard principles. Unlike the majority of other ISA designs, it is provided under the open source license, so it’s free to use for all the computer chip producers.

The RISC-V specification defines both 32-bit and 64-bit address space options, and additionally includes a description of a 128-bit flat address space variant. 

The RISC-V is a load–store architecture, using IEEE 754 floating-point instructions. RISC-V ISA also includes instruction bit field locations as a way to simplify the use of multiplexers in CPUs. 

Started with a goal to create a practical open source ISA that will be easily deployable in various hardware and software designs, including embedded systems, the RISC-V ISA is a continuation of a long history of CPUs architecture design projects developed at the University of California, Berkeley, since the late 1980s.

History of the RISC-V specification development

The project to develop RISC-V specification was originally started in 2010 by the University of California experts with an intent to create a practicable instruction set that will be available for practical use in various CPUs manufacturing. 

Dr. Krste Asanović, a professor of computer science at UC Berkeley, was an author of the project to develop RISC-V. Eventually, Dr David Patterson, another UC Berkeley professor and one of the creators of the original RISC chips back in the early 1990s, joined the project.

As any ISA needs to be stable for commercial use, the RISC-V Foundation was formed in 2015 with a goal to develop, maintain and publish the intellectual property related to the RISC-V specification. The original authors of the project at UC Berkeley have transferred all the rights to this non-profit corporation controlled by its members.

Currently, the RISC-V Foundation comprises over 325 members, including representatives from companies such as Google, NVIDIA, Microsemi, Western Digital. The RISC-V Foundation members participate in the development of the RISC-V ISA specification and related projects. 

In 2019, due to the U.S. trade regulations concerns as the main reason, the RISC-V Foundation relocated to Switzerland. In 2020, the organization was renamed as RISC-V International, becoming a Switzerland-registered nonprofit business association.

Today, the RISC-V International publishes all the documentation and specifications related to RISC-V designs, which remains open source and available for everyone to use free of charge. However, only the members of RISC-V International can vote to approve any changes to RISC-V specifications. 

ARM vs RISC-V Comparison 

Here’s a table comparing technical specifications of ARM and RISC-V. 

Final thoughts. ARM vs RISC-V: Which one to choose? 

As you can probably tell from the comparison chart above, there is no simple answer to this question. 

In many ways, right now, ARM-based CPUs are still a better option, mainly due to much longer lifecycle and the fact that ARM Ltd has invested billions of dollars into this specification over the years. ARM processors have a huge market share, being used in the majority of smartphones, as well as laptops and even PCs that are choosing ARM instead of x86 architecture-based designs. 

We could say, however, that RISC-Vs are the future and a very strong contender to the throne of the most used computer processors architecture. RISC-V can provide better performance using a minimum amount of power. The fact that RISC-V is open source and free to use by any processor manufacturers is also a huge advantage.

Some manufacturers, such as Western Digital, for example, have already started implementing the RISC-Vs in their microcontrollers attached to RAMs and SSDs. 

RISC-V is also getting increasingly popular in IoT devices and embedded systems of various kinds, due to its highly scalable nature. But it will undoubtedly take several years for industry players to transition to using RISC-V instead of ARM-based designs. 

The Tauro Technologies’ team of electronic engineers and designers has a proven track record of successfully designing custom hardware for various kinds of products in multiple technology fields. Drawing on the specific needs of our clients, we select and apply various engineering methods to electronic product development and manufacturing in order to achieve the desired result. Utilizing our in-house PCB assembly and debug expertise, we are able to build and evaluate your prototypes before high-volume manufacturing, rapidly and cost-efficiently. 

Interested to know more? Get in touch with us for details.

The post RISC-V vs ARM. Which One To Choose? appeared first on Tauro Technologies.