I already posted on one automotive panel at this year’s Arm TechCon. A second I attended was a more open-ended discussion on where we’re really at in autonomous driving. Most of you probably agree we’ve passed the peak of the hype curve and are now into the long slog of trying to connect hope to reality. There are a lot of challenges, … Read More
The market opportunities for machine learning hardware are becoming more succinct, with the following (rather broad) categories emerging:
We have learned from nature that two characteristics are helpful for success, diversity and adaptability. The same has been shown to be true for computing systems. Things have come a long way from when CPU centric computing was the only choice. Much heavy lifting these days is done by GPUs, ASICs, and FPGAs, with CPUs in a support … Read More
Building on an old chestnut, if sufficiently advanced technology looks like magic, there are a number of technology users who are increasingly looking like magicians. Of course when it comes to formal, neither is magical, just very clever. The technology continues to advance and so do the users in their application of those methods.… Read More
I previously wrote a blog about a session from Day 1 of the AI Hardware Summit at the Computer History Museum in Mountain View, CA, held just last week. From Day 2, I want to delve into this presentation by Bryan Bowyer, Director of Engineering, Digital Design & Implementation Solutions Division at Mentor, a Siemens Business.… Read More
I’ve talked before about Mentor’s work in high-level synthesis (HLS) and machine learning (ML). An important advantage of HLS in these applications is its ability to very quickly adapt and optimize architecture and verify an implementation to an objective in a highly dynamic domain. Design for automotive applications – for … Read More
Why does it seem like current FPGA devices work very much like the original telephone systems with exchanges where workers connected calls using cords and plugs? Achronix thinks it is now time to jettison Switch Blocks and adopt a new approach. Their motivation is to improve the suitability of FPGAs to machine learning applications,… Read More
Mentor are stepping up their game in AI/ML. They already had a well-established start through the Solido acquisition in Variation Designer and the ML Characterization Suite, and through Tessent Yield Insight. They have also made progress in prior releases towards supporting design for ML accelerators using Catapult HLS. Now… Read More
The basic principles used for neural networks have been understood for decades, what have changed to make them so successful in recent years are increased processing power, storage and training data. Layered on top of this is continued improvement in algorithms, often enabled by dramatic hardware performance improvements.… Read More
At the recent DesignCon 2019 in Santa Clara, I attended a couple of sessions where Cadence and their research partners provided some insight on machine learning/AI and on large system design analysis; with the first one focused on real-world cloud & machine learning/AI deployment for hardware design and the second one focused… Read More
