Excerpt from article: “Why Go Custom in AI Accelerators, Revisited”
I believe I asked this question a year or two ago and answered it for the absolute bleeding edge of datacenter performance – Google TPU and the like. Those hyperscalars (Google, Amazon, Microsoft, Baidu, Alibaba, etc) who want to do on-the-fly recognition in pictures so they can tag friends in photos, do almost real-time machine translation, and many other applications. But who else cares? I’ve covered a couple of Siemens EDA events on using Catapult HLS to build custom accelerators. Fascinating stuff and good insights to the methods and benefits, but I wanted to know more about what kind of applications are using this technology. I talked to the Catapult group to get some answers: Mike Fingeroff (technologist for Catapult), Russ Klein (Product Marketing for Catapult) and Anoop Saha (Senior manager, strategy and Biz Dev for machine learning and 5G).
Russ talked about his Ring doorbell. He doesn’t want the doorbell to go off at 3am because it detected a cat nearby. He wants accurate detection at a good inference rate, but it has to be very low power because the doorbell may be running on a battery. I could imagine a similar point being made for an intelligent security system. The movie trope of detectives fast forwarding through hours of CCTV video may soon be over. A remote camera shouldn’t upload video unless it sees something significant, because uploads burn power at the camera and because who wants to scroll through hours of nothing interesting happening?
Read the entire article on SemiWiki originally published on June 24th, 2020.