Kurt Shuler, VP Marketing at Arteris IP, provides more insight into what's happening in this highly dynamic space in the latest SemiWiki blog written by Bernard Murphy (SemiWiki):
Why High-End ML Hardware Goes Custom
January 30th, 2019 - By Bernard Murphy
In a hand-waving way it’s easy to answer why any hardware goes custom (ASIC): faster, lower power, more opportunity for differentiation, sometimes cost though price isn’t always a primary factor. But I wanted to do a bit better than hand-waving, especially because these ML hardware architectures can become pretty exotic, so I talked to Kurt Shuler, VP Marketing at Arteris IP, and I found a useful MIT tutorial paper on arXiv. Between these two sources, I think I have a better idea now.
Start with the ground reality. Arteris IP has a bunch of named customers doing ML-centric design, including for example Mobileye, Baidu, HiSilicon and NXP. Since they supply network on chip (NoC) solutions to those customers, they have to get some insight into the AI architectures that are being built today, particularly where those architectures are pushing the envelope. What they see and how they respond in their products is revealing.
You can learn more about what Arteris IP is doing to support AI in these leading-edge ML design teams HERE. They certainly seem to be in a pretty unique position in this area.
For more information, download this FlexNoC AI Package datasheet; http://www.arteris.com/flexnoc-ai-package
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