Evolving Edge Computing


Edge computing is a term that has been in use for a long time. Throughout the industry, there are many references to edge and many pre-conceptions about what that might mean. The term ‘edge’ is typically used for devices that exist on the edge of a network and can cover a plethora of use cases, ranging from the router in your house, a smart video camera surveying a parking lot, to a control... » read more

From Data Center To End Device: AI/ML Inference With GDDR6


Created to support 3D gaming on consoles and PCs, GDDR packs performance that makes it an ideal solution for AI/ML inference. As inference migrates from the heart of the data center to the network edge, and ultimately to a broad range of AI-powered IoT devices, GDDR memory’s combination of high bandwidth, low latency, power efficiency and suitability for high-volume applications will be incre... » read more

Flexible USB4-Based Interface IP Solution For AI At The Edge


Consumers have become accustomed to smart devices that are powered by advances in artificial intelligence (AI). To expand the devices’ total addressable market, innovative device designers build edge AI accelerators and edge AI SoCs that support multiple use cases and integration options. This white paper describes a flexible USB4-based IP solution for edge AI accelerators and SoCs. The IP so... » read more

Challenges In Developing A New Inferencing Chip


Cheng Wang, co-founder and senior vice president of software and engineering at Flex Logix, sat down with Semiconductor Engineering to explain the process of bringing an inferencing accelerator chip to market, from bring-up, programming and partitioning to tradeoffs involving speed and customization.   SE: Edge inferencing chips are just starting to come to market. What challenges di... » read more

ACAP At The Edge With The Versal AI Edge Series


This white paper introduces the AI Edge series to the Versal ACAP portfolio, a domain-specific architecture (DSA) that meets the strenuous demands of systems implemented in the 7nm silicon process. This series is optimized to meet the performance-per-watt requirements of edge nodes at or near the analog-digital boundary. Here, immediate response to the physical world is highly valued, and in ma... » read more

Architectural Considerations For AI


Custom chips, labeled as artificial intelligence (AI) or machine learning (ML), are appearing on a weekly basis, each claiming to be 10X faster than existing devices or consume 1/10 the power. Whether that is enough to dethrone existing architectures, such as GPUs and FPGAs, or whether they will survive alongside those architectures isn't clear yet. The problem, or the opportunity, is that t... » read more

AI: A Perfect Solution But At What Cost?


The advancement of artificial intelligence (AI) has been a great enabler for the Internet of things (IoT). Given the ability to think for itself, it’s shrugged off its original definition as a network of tiny sensors and grown to incorporate a host of more intelligent AIoT (AI+IoT) devices, from smartphones all the way up to autonomous vehicles. AI has also paved the way for new IoT device... » read more