Find Top SoC Solutions
for AI, Automotive, IoT, Security, Audio & Video...

AI Edge Inference is Totally Different to Data Center

www.eetimes.com, Jul. 23, 2020 – 

While inference accelerators started out primarily in the data center, they have quickly moved to edge inference with applications such as autonomous driving and medical imaging. Through this transition, customers are finding out, often the hard way, that the same accelerator that did so well processing images in the data center fails badly in edge inference. The reason for this is simple: one processes a pool of data while the other processes a stream.

Streaming throughput is when you process at batch = 1 and a pool is when you process batch = many. In the data center, customers are typically processing pools of data such as photos that are being tagged. The goal is getting through as many photos as possible with the least amount of resources and power consumption, and best latency.

click here to


Partner with us


List your Products

Suppliers, list and add your products for free.

More about D&R Privacy Policy

© 2020 Design And Reuse

All Rights Reserved.

No portion of this site may be copied, retransmitted, reposted, duplicated or otherwise used without the express written permission of Design And Reuse.