Arteris Articles

SemiWiki: Safety: Big Opportunity, A Long and Hard Road

Kurt Shuler, VP Marketing at Arteris IP, explains the support and business cycle from the vendor to the integrator in the latest SemiWiki blog written by Bernard Murphy:

Safety: Big Opportunity, A Long and Hard Road

February 27th, 2019 - By Bernard Murphy

Still think you want to sell IP into the automotive chain? Bernard Murphy (SemiWiki) distills Kurt Shuler insights into what this takes. There’s certainly a lot of promise. More big chips in the central brain and in intelligent sensors together offer a lot of opportunity. The US, Europe and Israel markets are all very aggressive in developing ADAS and ML. China has been a laggard but is coming on strong and is not held back by legacy so much. They also see a big tie-in with AI where they are very strong. Kurt says there are more than a couple of hundred funded startups in automotive and AI in China.

That said, this is not an easy way to get rich. You’ll have to put a lot of investment into supporting your customers, supporting their customers and so on up to the top. The market is very dynamic, so what “done” means may not always be clear. You may not be paid for quite a long time. But if you have the grit to hang on and keep your customer happy the whole way through, you might just be successful!


 For more information, download this FlexNoC AI Package datasheet; http://www.arteris.com/flexnoc-ai-package

Topics: ISO 26262 semiconductor semiwiki kurt shuler flexnoc ai package noc interconnect ML-centric design

SemiWiki: Why High-End ML Hardware Goes Custom

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

Topics: NoC semiconductor semiwiki kurt shuler AI chips flexnoc ai package accelerators noc interconnect ML-centric design