Presented by Linley Gwennap, Principal Analyst, The Linley Group.
In the data center, new architectures are emerging to challenge the GPU's dominance in AI training and inference. Embedded systems such as smart cameras, smart vehicles, and smart robots require powerful accelerators. AI accelerators are even moving into IoT and smart-home devices, running simple neural networks on milliwatts of power. This presentation will describe the latest trends in AI acceleration while addressing how these accelerators are used across this range of end applications.
The Linley Spring Processor Conference featured technical presentations on AI acceleration, targeting edge, automotive, IoT, and data center. Also covered were new CPU architectures, networking, security, SoC design, and other processor-related topics.
Proceedings from the event are available for download. [ Ссылка ]
For more on this topic, check out our latest report, "A Guide to Processors for Deep Learning"
[ Ссылка ]
The report covers processors for accelerating deep learning, neural networks, and vision processing for AI training and inference in data centers, autonomous vehicles, and client devices.
