As the demand for artificial intelligence continues to rise in various industries, from healthcare and finance to manufacturing and autonomous vehicles, industrial computers face the challenge of optimizing AI workloads. Developers are constantly seeking efficient and scalable solutions to solve these challenges. One such solution is using COM Express , a standardized form factor that can be used as a flexible computing platform for various AI workloads.

With the ability to choose from wide variety of CPUs and the flexibility to right-size CPU to target various AI workloads, COM Express empowers organizations to create efficient, scalable, and cost-effective AI solutions. In addition to harnessing the advantages of COM Express, developers can leverage additional AI accelerators to further optimize the solutions. COM-HPC, a new specification, further enables enhanced performance and scalability for high-performance computing applications.

The Intel Alder Lake x86 CPU is an ideal solution for COM Express modules targeting AI workloads due to built-in AI acceleration with Intel Deep Learning Boost technology. This integrated AI capability allows for efficient execution of AI workloads, such as neural network inference and deep learning tasks. By leveraging the built-in AI accelerator, COM Express modules based on Alder Lake can provide optimized performance for AI applications without the need for additional external accelerators.

What is COM Express?

COM Express is a highly integrated and compact computer on module that is designed to offer scalability and flexibility by providing a standardized form factor and interface for integrating different processor architectures and I/O configurations. Introduced by the PCI Industrial Computer Manufacturers Group in 2005, COM Express provides a single circuit board with integrated RAM.

This family of modular, small form factor modules has gained significant traction in various industries, including automation, gaming, retail, transportation, robotics, and medical fields. With eight different types, four sizes, and three major revisions, COM Express promotes vendor technology reuse while catering to mid-range edge processing and networking requirements.

The key differentiator of COM Express from traditional single-board computers (SBCs) lies in its ability to plug off-the-shelf modules into custom carrier boards designed for specific applications. This enables an upgrade path for the CPUs while keeping the carrier board intact. By using a custom COM Express carrier board, all necessary signals can be efficiently routed to the peripherals, while COM Express processor modules serve as the main controller. These advanced features ensure the versatility and adaptability of COM Express for diverse application requirements.

Comparing COM-HPC with COM Express

COM-HPC is an evolution of the COM Express standard, uniquely tailored to address the demands of high-performance computing applications. With its focus on enhanced performance, scalability, and advanced features, COM-HPC caters to the same applications and markets as COM Express, but with notable differentiators. It boasts higher-end CPUs, expanded memory capacity, and increased and faster I/O capabilities. It’s essential to emphasize that COM-HPC does not aim to replace COM Express, rather the two standards exist as distinct entities in the field of embedded computing, offering developers a broader spectrum of choices to meet specific application requirements.

COM-HPC brings significant improvements over COM Express for AI workloads, particularly in terms of PCIe lanes and PCIe generation support:

• Increased PCIe Lanes: One of the key advantages of COM-HPC over COM Express is the availability of more PCIe lanes. COM Express has a limited number of PCIe lanes, which can restrict the connectivity options and the number of I/O interfaces or accelerators that can be integrated. In contrast, COM-HPC modules provide a higher number of PCIe lanes, allowing for more extensive connectivity and the integration of multiple high-speed devices.
• PCIe Gen4/5 Support: Another crucial enhancement in COM-HPC is the support for PCIe Gen4 and Gen5, whereas COM Express supports up to PCIe Gen3. PCIe Gen4 and Gen5 offer higher data transfer rates and improved bandwidth compared to Gen3. This is particularly advantageous for AI workloads that require fast data movement between the CPU, GPU, storage devices, and other peripherals.

In summary, newer generation processors, paired with higher data rates, dramatically lower the size, power and cost requirements of the systems required to perform the AI tasks.

The Advantages of Choosing COM Express for AI Workloads

COM Express offers several distinct advantages when it comes to AI workloads. As a flexible and scalable platform, it provides developers to adapt their AI systems according to specific requirements like CPU performance, power requirements. They can then design a carrier board that integrates the module with additional AI-specific components, such as AI accelerators. Below is the block diagram example of COM Express platform with AI Accelerator.

Here are the key advantages of choosing COM Express (or COM-HPC) for AI workloads:

• Flexibility and Scalability: COM Express allows developers to choose from a wide range of CPU options. Such kind of flexibility allows them to choose the module that best matches the computing needs of their AI workloads. Whether it’s a complex neural network inference or deep learning task, the platform can be customized to deliver optimal performance.
• Modular Design: COM Express follows a modular design approach with a separate CPU module and carrier board. This modularity simplifies system customization and future upgrades. Developers can easily swap out or upgrade the CPU module without redesigning the entire system, saving time and effort while adapting to evolving AI requirements.
• Streamlined Integration: COM Express adheres to industry-standard form factors and interfaces, ensuring compatibility across different vendors. This standardized approach simplifies system integration, reducing development complexity and time to market. Developers can focus on optimizing their AI algorithms and software, confident that the hardware integration will be seamless.
• Rich Connectivity Options: COM Express provides a wide array of interfaces, including Ethernet, USB, PCIe, and DisplayPort interfaces. These interfaces enable effortless integration with various peripherals, sensors, and external devices commonly used in AI applications. The rich connectivity options enhance data I/O capabilities, facilitating efficient communication and interaction within the AI system.
• Long-Term Availability and Support: COM Express offers long-term availability and support, ensuring continuity for AI deployments. This is particularly crucial for industries that rely on stable and long-lasting AI systems. With a consistent platform and extended availability, developers can plan for long-term deployment and maintenance, with access to software updates and technical assistance.
• Cost Optimization: COM Express provides a cost-effective solution for AI workloads. By leveraging COM Express, developers can save on development costs and reduce time to market. The modular design allows for efficient resource allocation, ensuring optimal performance while minimizing unnecessary expenses.
• Time to Market: Since the computer modules are widely available in the embedded marketplace, COM Express enables developers to focus on the IO needs, the addition of accelerators, the AI models and application software.

Real-World Applications of COM Express for AI Workloads

As stated above, COM Express modules offer immense potential for developers to optimize AI workloads on industrial computers, leading to transformative impacts and various implications for cost-effective solutions and large-scale deployments. Let’s delve into real-world examples and insights to showcase the significance of this optimization trend.

In the field of autonomous vehicles, this optimization trend allows autonomous vehicles to navigate complex environments, enhancing safety and efficiency. By leveraging COM Express modules, developers can achieve cost-effective solutions by utilizing existing industrial computers and upgrading them with optimized AI capabilities, resulting in large-scale deployments of autonomous vehicles across transportation networks.

Industrial automation is another area where COM Express systems can revolutionize AI workloads. By optimizing AI algorithms on industrial computers using COM Express modules, developers can achieve significant cost savings and efficiency gains in manufacturing processes. For instance, AI-powered computer vision systems can inspect and detect defects in real-time, improving quality control and reducing production costs. The use of COM Express modules enables industrial computers to handle these AI workloads effectively, making cost-effective solutions viable for large-scale deployment in manufacturing facilities.

In the healthcare sector, COM Express systems can optimize AI workloads on industrial computers to improve diagnostics, patient monitoring, and personalized treatment. For example, by leveraging COM Express systems, developers can enable industrial computers to process complex medical imaging data and apply AI algorithms for more accurate and timely diagnosis. This optimization trend in AI workloads allows healthcare providers to deliver cost-effective, benefiting patients globally.

What to choose

AI accelerators are paired with COM Express module on the carrier as separate modules or integrated directly into the carrier board’s design. This modular approach provides scalability and flexibility, allowing system designers to customize AI processing capabilities to meet the specific requirements of their applications. It also enables easy upgrades or replacements of AI accelerators without having to modify the entire system, making it both cost-effective and future-proof. AI accelerators such as Blaize, Hailo or Axelera paired with COM Express module can provide significant benefits. For example, combining Axelera M.2 AI Edge accelerator module with COM Express Carrier board can achieve up to 120 TOPS of AI performance with the flexibility of switching between the CPU families for optimized compute needs.

These accelerators are specifically designed to enhance AI workloads and provide optimized compute capabilities compared to GPUs. This level of compute power can greatly benefit vision processing applications, which often require intensive computations for tasks such as object detection and classification.

Conclusion

COM Express and COM-HPC offer flexible and scalable platform to enable various AI workloads, allowing developers to customize their systems based on CPU performance, power requirements, and I/O interfaces. CPUs like Intel Alder Lake integrated into COM Express modules provide efficient AI execution, integrated graphics performance, enhanced compute density, ecosystem support, and broad connectivity options. The combination of the CPU with optional AI Accelerator delivers optimized performance, reducing costs and enabling efficient large-scale AI deployments.

With the Tauro Technologies’ team of electronic engineers and designers it becomes possible to design and deploy comprehensive AI processing systems based on x86 and ARM CPUs paired with various AI Accelerators. This strategic approach helps bring down costs and ensures the right balance between compute power and AI processing needed for the system. We can customize the I/O as well as the footprint to fit your application requirements.

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

The post Leveraging COM Express and COM-HPC for AI Workloads appeared first on Tauro Technologies.

As technology evolves, the automotive industry is constantly seeking ways to make driving safe, reliable, and autonomous. In this blog post, we’ll explore the features, functionality, and the impact that a platform based on dual NVIDIA’s AGX Orin modules offers for the future of vehicle safety during operation. Additionally, we will elaborate on the concept of safety-critical systems and highlight the distinctions between safety-critical functionalities and ADAS (Advanced Driver Assistance System).

The Jetson AGX Orin is designed for advanced robotics and AI edge applications for manufacturing, logistics, retail, service, agriculture, smart city, healthcare, and life science. Dual Orin (2 Orin devices on the same motherboard) offers system redundancy, which refers to the presence of backup or duplicate components that can take over in the event of a failure in the primary system.

ADAS provides driver assistance and convenience, but it is not solely responsible for critical functions that impact safety. Safety-critical functions encompass components directly involved in critical functions such as braking and collision avoidance. Safety-critical systems follow strict standards to ensure reliable operation.

What is Orin?

The NVIDIA Jetson Orin solution is a SOM (system-on-module) with CPU, GPU, memory, power management, and various high-speed interfaces embedded on a single board. NVIDIA Jetson brings accelerated AI performance to the edge in a power-efficient and compact form factor. The Jetson family of modules all use the same NVIDIA CUDA-X™ software, and support cloud-native technologies like containerization and orchestration to build, deploy, and manage AI at the edge.

NVIDIA’s Orin platform (SoC) has three series for its Jetson products:

• Jetson AGX Orin series
• Jetson Orin NX series
• Jetson Orin Nano series

NVIDIA Jetson Orin modules provide 275 TOPS of AI performance and which increases the performance 8 times compared to Jetson Xavier for multiple concurrent AI inference pipelines, in addition to high-speed interface support for multiple sensors.

One of the major features of NVIDIA Jetson Orin is the DLA (Deep Learning Accelerator) which supports next-generation NVDLA 2.0 with 9x the performance of NVDLA 1.0. It enables the GPU to run more complex networks and dynamic tasks.

A Comparison of Orin with Traditional CPU/GPU

Now, let’s delve into a comparison between traditional processors and Orin by examining the following key features:

Architecture

NVIDIA Jetson Orin is designed specifically for autonomous machines and edge computing. Jetson AGX Orin modules feature the NVIDIA Orin SoC with a NVIDIA Ampere architecture GPU, Arm® Cortex®-A78AE CPU, next-generation deep learning and vision accelerators, and a video encoder and a video decoder making it highly optimized for tasks like computer vision, deep learning, and robotics.

Traditional CPUs (Central Processing Units) and GPUs (Graphics Processing Units) are more general-purpose processors designed for a wide range of computing tasks, including running operating systems, executing applications, and performing graphics rendering.

Power Efficiency

NVIDIA Jetson AGX Orin series modules are designed with a high-efficiency Power Management Integrated Circuit (PMIC), voltage regulators, and a power tree to optimize power efficiency. It strikes a balance between performance and energy consumption, allowing for longer battery life and reduced power requirements in embedded systems.

While traditional CPUs and GPUs can offer high computational power, they are generally more power-hungry compared to specialized SoCs like Jetson Orin. They are commonly found in desktops, servers, and workstations where power consumption is less constrained.

AI Performance

The NVIDIA Jetson AGX Orin series provides server class performance, delivering up to 275 TOPS of AI performance for powering and managing autonomous systems. Its high performance is ideal for tasks like object detection, image recognition, natural language processing, and autonomous navigation.

Traditional CPUs and GPUs can also handle AI workloads, but they do not provide the same level of performance or efficiency as AI-focused modules like Jetson Orin. GPUs, in particular, have been utilized for parallel processing in deep learning tasks, but they are less power-efficient compared to specialized AI chips.

In addition, the Jetson Orin modules are extremely compact, enabling the compute platform to have reduced size and weight – critical for autonomous robots and UAVs.

Software Ecosystem

NVIDIA Jetson Orin is part of NVIDIA’s Jetson platform, which offers a comprehensive software stack, including drivers, libraries, and frameworks specifically optimized for AI and autonomous applications. It supports popular AI frameworks like TensorFlow, PyTorch, and CUDA, providing developers with familiar tools and resources.

Traditional CPUs and GPUs also have a mature and extensive software ecosystem with support for a wide range of operating systems, development tools, and programming languages. They are compatible with various software frameworks, including those used for AI, but may require additional configuration and optimization for specific AI workloads.

Key differences between NVIDIA Orin and Xavier

NVIDIA Jetson AGX Xavier and NVIDIA Jetson AGX Orin have the same physical footprint and are pin compatible while also being in the same price range with one major difference that the Orin offers much higher performance.

The biggest change change is moving from Nvidia’s Carmel CPU clusters to the ARM Cortex-A78AE on Jeston AGX Orin.
The Orin CPU complex is made up of 12 2.2 GHz cores, each with 64KB Instruction L1 Cache and 64KB Data Cache, and 256 KB of L2 Cache. This enables x1.85 performance increased compared to the eight core Carmel CPU on Jetson AGX Xavier.

Jetson AGX Orin modules deliver an AI performance that can reach 275 TOPS with up to 64 GB of memory, compared to 32 TOPS with up to 32 GB of memory for Jetson Xavier.

Jetson AGX Orin 64GB has 2048 CUDA cores and 64 Tensor cores with up to 170 Sparse TOPS of INT8 Tensor compute, and up to 5.3 FP32 TFLOPs of CUDA compute, while Jetson Xavier has only up to 1.4 FP32 TFLOPs of CUDA compute. Ampere GPU brings support for sparsity, a fine-grained compute structure that doubles throughput and reduces memory usage.

DLA 2.0 provides a highly energy efficient architecture. With this new design, NVIDIA increased local buffering for even more efficiency and reduced DRAM bandwidth. DLA 2.0 additionally brings a set of new features including structured sparsity, depth wise convolution, and a hardware scheduler. This enables up to 105 INT8 Sparse TOPs total on Jetson AGX Orin DLAs compared with 11.4 INT8 Dense TOPS total on Jetson AGX Xavier DLAs.

The 12-core CPU on Jetson AGX Orin 64GB enables 1.85 times the performance compared to the 8-core NVIDIA Carmel CPU on Jetson AGX Xavier. Customers can use the enhanced capabilities of the Cortex-A78AE including the higher performance and enhanced cache to optimize their CPU implementations.

Jetson AGX Orin modules bring support for 1.5 times the memory bandwidth and 2 times the storage of Jetson AGX Xavier, enabling 32GB or 64GB of 256-bit LPDDR5 and 64 GB of eMMC. The DRAM supports a max clock speed of 3200 MHz, with 6400 Gbps per pin, enabling 204.8 GB/s of memory bandwidth.

The combination of NVIDIA’s processing capabilities and power efficiency, along with its safety-critical features, makes it the ideal solution for autonomous applications.

Safety Critical Software in Automotive Safety

Functional safety in processor-based systems is particularly critical in automotive applications. Apart from the ongoing shift towards autonomous vehicles, cars are increasingly dependent on microprocessors to carry out essential operations and must have redundant systems to enable safety in the event of a component failure.

ISO 26262 serves as the globally recognized standard for ensuring functional safety in the automotive industry. This international standard encompasses both the hardware and software components of a vehicle’s electrical and electronic (E/E) systems. Throughout the development process, ISO 26262 outlines specific requirements that must be fulfilled to ensure the safety-related functionality of the system, along with the corresponding processes, methodologies, and tools. By adhering to the ISO 26262 standard, manufacturers can ensure that sufficient safety measures are implemented and maintained throughout the entire lifespan of the vehicle.

ISO 26262 offers comprehensive guidelines on determining acceptable risk levels for systems or components and documenting the testing process. It encompasses the following key aspects:

• Defines an automotive safety lifecycle that covers management, development, production, operation, service, and decommissioning stages, allowing for customization of activities during each phase.
• Implements an automotive-specific risk-based approach for classifying risk levels known as Automotive Safety Integrity Levels (ASILs).
• Utilizes ASILs to specify the required safety measures for achieving an acceptable residual risk.
• Establishes requirements for validation and confirmation measures to ensure the attainment of a satisfactory level of safety.y

Dual AGX Orin Controller Overview

The Dual AGX Orin system offers superior computing power compared to a single Orin solution, making it preferable for specific applications that require higher computational power and redundancy.

The Dual Orin Controller’s computational capacity enables it to handle multiple complex tasks simultaneously. This capability is particularly valuable in scenarios where there is a need for concurrent processing of multiple data streams from various sensors, making it suitable for advanced autonomous machines, commercial vehicles, unmanned distribution vehicles, and unmanned cleaning vehicles.

In safety-critical applications, redundancy is essential to ensure system reliability. The Dual Orin Controller’s utilization of two AGX Orin modules provides a level of redundancy and failover capabilities. If one module encounters an issue, the other can continue functioning, minimizing the risk of critical system failures and improving the overall reliability of the autonomous machine.

Tauro Technologies TT300 Dual AGX Orin Controller

Tauro Technologies’ TT300 Dual AGX Orin compute platform provides exceptional computing power, low energy consumption, in a compact form factor.

With up to 400/550 TOPS of AI performance this product can be used in autonomous vehicles, UAVs and robotics. The product is designed for high reliability and redundancy, provides multi-sensor clock synchronization with sub-nanosecond accuracy and millisecond latency for precise timing.

Let’s take a closer look at TT300 key features:

Dual Orin Controllers 550 TOPS

The TT300 board is equipped with two powerful Orin controllers, delivering combined processing power of 550 TOPS. This immense computing power enables lightning-fast data processing and analysis, making it ideal for handling complex AI workloads.

Infineon TC397 Safety MCU

Ensuring the highest levels of safety and reliability, the TT300 board incorporates the Infineon TC397 safety microcontroller to support safety requirements up to ASIL-D. This MCU plays a crucial role in safeguarding the system against potential hazards and maintaining the integrity of critical operations.

100Base-T1/1000Base-T1 Ethernet

To facilitate efficient and reliable data communication, the TT300 board is equipped with both 100Base-T1 and 1000Base-T1 Ethernet interfaces. These interfaces enable fast and secure data transfer, ensuring smooth integration into existing vehicle network infrastructures.

Wi-Fi/4G/5G

TT300 board supports Wi-Fi, 4G LTE and 5G connectivity, enabling seamless wireless communication and remote access. Whether you need to stream data, receive updates, or control the board remotely, these connectivity features have you covered.

• GMSL2 Interface for Hi-Res Cameras

The TT300 board features a GMSL2 interface, enabling reliable connection with high-resolution cameras. This interface supports the transmission of data between the controller and cameras, ensuring high-quality image and video feed for AI applications such as ADAS, object detection, tracking, and recognition.

GMSL cameras are becoming a defacto standard in automotive industry where high data rates and long-distance support is required, addressing the need to transport higher video data rates in automotive video systems.
In addition to high bandwidth transmission, long-distance support, and low latency, GMSL cameras also come with the following features:

• Virtual channel support
• GMSL1 and GMSL2 backward compatibility
• Video duplication
• Automatic Repeat Request (ARQ) feature
• Compatibility with ARM platforms like the NVIDIA Jetson series

I/O Capabilities

TT300 is powered by two NVIDIA Jetson AGX Orin modules and Infineon TC397 safety MCU enables the design to meet ASIL-D highest reliability requirements. The I/O capabilities of the product include automotive as well as industrial ethernet interfaces, USB, wireless connectivity over 4G/5G and Wi-Fi, GMSL camera and LVDS radar interfaces for ADAS applications, as well as CAN and LIN interfaces for automotive and robotics applications routed to CMC connector. Wide selection of interfaces and customization options makes this device easily adaptable to various use cases and application scenarios.

Conclusion

Tauro Technologies’ TT300 is one of the industry’s first platforms to offer the NVIDIA Jetson Orin AGX in a redundant safety-critical setting. This is an ideal system for self-driving vehicles in automotive, mining, and defense sectors as well as autonomous robots and UAVs that require exceptional performance and functional safety certification.
We can customize the I/O as well as the product packaging to fit your application requirements – contact us for details.

The post Dual Orin Controller: The Ideal Safety-Critical Platform for Autonomous Vehicles appeared first on Tauro Technologies.

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.

The demand for embedded systems across various industries and technology fields today is as high as ever before. Embedded systems are essential to many electronic devices and automated solutions that we are increasingly relying upon. So it comes as no surprise that the global embedded systems market is rapidly growing. According to a recent study, the total size of the embedded systems market is expected to reach $116.2bn by 2026 from $86.5bn last year, growing at a CAGR of 6.3% from 2021 to 2026. 

Even the COVID pandemic and global economic turbulence caused by this healthcare crisis weren’t able to disrupt the consistent growth of the embedded systems market. As the authors of an older market study noted, even though low demand for consumer electronic devices due to COVID lockdowns had its negative impact, it was balanced by the increasing need for various embedded hardware components for the healthcare industry. 

Six most interesting embedded systems industry trends in 2022 

The abundance of technological and market development trends is another sign that we have all the reasons to feel optimistic about the evolution of the embedded systems industry going forward. 

 Let’s take a closer look at some of the most interesting and noteworthy trends that, in our opinion, will influence the embedded systems industry in 2022 and over the next few years. 

Automotive industry driving embedded systems market growth

When it comes to the applications of embedded systems, the automotive industry today is one of the main drivers of market growth. This trend will most likely further increase in 2022 as well, fueled by continuously rising demand for electric and hybrid vehicles across the globe. The manufacturers of electric and hybrid vehicles rely on embedded systems in a variety of smart electronic components such as advanced driver-assistance systems (ADAS), power control units, engine cooling systems, etc. Additionally, automotive and mobile robotics industries are also rapidly adopting autonomous technologies and further require the integration of LIDAR, camera, sensor and power subsystems. All these components rely on embedded systems for centralization and coordination on processes. 

Explosive demand for military embedded systems 

As you may know, embedded systems play a vitally important role in many devices and electronic machine components used for military applications. The demand for weaponry and advanced military equipment has already been on the rise in recent years as a result of escalating regional tensions and geopolitical rivalry around the globe. And we can expect the boost in development of the military embedded systems market as most NATO countries are significantly increasing their defense budgets. Military embedded systems are used in land, sea, and air warfare theaters for a large variety of applications, including unmanned vehicles, counter UAV systems, surveillance systems, weapons guidance systems, communication equipment, command and control solutions, satellite communications, etc. 

Following the evolution of military systems, we can clearly see that not just the commercial sector companies are looking to implement AI, 5G, cloud computing, and other technological innovations. Many defense contractors are also interested in tech innovations as a way to produce more advanced systems. 

Wider AI and ML integration 

Artificial intelligence (AI) has been one of the most significant technology trends in recent years. AI and ML (machine learning) solutions continue to gain momentum and spread across a variety of industries and market segments. 

The embedded systems are not an exception, even though AI and ML solutions traditionally have been challenging to implement in embedded systems due to their hardware and framework limitations. But new hardware solutions along with innovative techniques used for inference processing, data curation and performance acceleration help to overcome these obstacles. In 2022, we expect to see even more new embedded implementations leveraging AI and ML technologies. 

NVIDIA products are widely used for training and inferencing applications in many AI systems, and Tauro Technologies has been building these systems from their early days. As the industry evolves, other silicon and software solutions are emerging that promise to offer better price–performance ratio for many machine vision applications.

Embedded security and defense against cyber threats 

Cyberattacks and information security breaches have been on the rise for a number of years now. And it’s not a secret that embedded systems are known to be vulnerable to hacker attacks and cybersecurity threats of various kinds. There are multiple reasons why embedded systems often fail to provide the appropriate level of protection against cyber threats: poor access control or authentication settings, no regular security updates, remote deployment, reliance on legacy hardware, etc. 

This is why the development of embedded security software and hardware is on the rise in recent years, as well as the standards for the security level in embedded hardware designs. Specifically, we have noticed a rise of embedded systems that implement TPM, AES encryption, and FIPS 140 technologies on hardware platforms.

 5G technologies and 5G-based embedded systems 

The ongoing deployment of 5G infrastructure is expected to be a major growth driver for a variety of technology fields, mainly telecommunications, industrial automation, internet of things (IoT), automotive, etc. The demand for embedded systems based on 5G architecture will also be increasing along with overall 5G implementation progress. Growing communications and processing speed, achieved with 5G architecture, without a doubt will be very helpful to solve the performance issues typical for embedded systems based on communication standards of previous generations.

Virtual and augmented reality with embedded systems

Virtual reality (VR) and augmented reality (AR) is another major tech industry niche that has been trending for a while, keeps gaining momentum year after year, and received an additional boost thanks to COVID pandemic and increasing global turbulence overall. VR/AR solutions have a wide range of cost-saving and efficiency-improving applications. Modern-day feature-rich virtual environments cannot function without complex high-performance embedded systems. They allow VR/AR solutions to match movements of the user with rendering of graphics, sound and text in real time. We have seen early applications of VR/AR in skills development and training both for industrial and military purposes, which is why we expect the demand for such complex VR/AR embedded systems to increase in 2022 as well. 

Final thoughts 

Some of the other notable embedded systems industry trends that we didn’t mention in this article are the rapidly growing real-time segment of the market, rising popularity of Python as the main programming language for embedded systems software, related IoT development trends, and more. 

What’s also worth mentioning, all six industry trends described above are connected and, in many ways, are fueling each other’s growth. For example, the demand for autonomous vehicles, robotics and AI technologies in commercial and military systems drives the demand for faster 5G communication that can enable the network speed required to fully implement these tech innovations.

Based on the foregoing, it is safe to say that the demand for embedded systems across market niches and applications 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 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 Six Key Embedded Systems Industry Trends in 2022 appeared first on Tauro Technologies.