Holoscan Platform for Robotics and Edge AI Ethernet Sensor Bridges and the Next Generation of Edge AI Systems For more than a decade, embedded vision systems have relied on two dominant interfaces: MIPI-CSI and GMSL. These standards were good enough for automotive ADAS, drones, and early robotics. They offered reliability and adequate bandwidth at…
Leveraging COM Express and COM-HPC for AI Workloads 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…
Use of GPUs in Edge AI Computing 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…
Advantages in Machine Vision on the Edge Efficiency of Artificial intelligence (AI) utilizing vision systems to manage autonomous vehicles and robots is at the core of well designed unmanned system. Putting that AI processing close to the machine vision cameras and time-of-flight sensors, offers a breakthrough for a wide range of manufacturing and logistics applications. …