Can you name the EDA vendor that first used AI starting 15 years ago for circuit designers using SPICE simulators? I can remember that vendor, it was Solido, now part of Siemens EDA, and I just read their 8 page paper on how they look at the various levels of AI being used in EDA to help IC designers work smarter and faster than using manual… Read More
Tag: ML
proteanTecs On-Chip Monitoring and Deep Data Analytics System
State-of-the-art electronics demand high performance, low power consumption, small footprint and high reliability from their semiconductor products. While this imperative is true across many different market segments, it is critical for applications such as the automotive/autonomous driving and data centers. As electronic… Read More
Using ML for Statistical Circuit Verification
I’ve been following Solido as a start-up EDA vendor since 2005, then they were acquired by Siemens in 2017. At the recent User2User event there was a presentation by Kwonchil Kang, of Samsung Electronics on the topic, ML-enabled Statistical Circuit Verification Methodology using Solido. For high reliability circuits… Read More
Quadric’s Chimera GPNPU IP Blends NPU and DSP to Create a New Category of Hybrid SoC Processor
Performance, Power and Area (PPA) are the commonly touted metrics in the semiconductor industry placing PPA among the most widely used acronyms relating to chip development. And rightly so as these three metrics greatly impact all electronic products that are developed. The degree of impact depends of course on the specific … Read More
Machine Learning Applications in Simulation
Machine learning (ML) is finding its way into many of the tools in silicon design flows, to shorten run times and improve the quality of results. Logic simulation seemed an obvious target for ML, though resisted apparent benefits for a while. I suspect this was because we all assumed the obvious application should be to use ML to refine… Read More
Intellectual Abilities of Artificial Intelligence (AI)
To understand AI’s capabilities and abilities we need to recognize the different components and subsets of AI. Terms like Neural Networks, Machine Learning (ML), and Deep Learning, need to be define and explained.
In general, Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed… Read More
Intelligently Optimizing Constrained Random
“Who guards the guardians?” This is a question from Roman times which occurred to me as relevant to this topic. We use constrained random to get better coverage in simulation. But what ensures that our constrained random testbenches are not wanting, maybe over constrained or deficient in other ways? If we are improving with a faulty… Read More
HLS in a Stanford Edge ML Accelerator Design
I wrote recently about Siemens EDA’s philosophy on designing quality in from the outset, rather than trying to verify it in. The first step is moving up the level of abstraction for design. They mentioned the advantages of HLS in this respect and I refined that to “for DSP-centric applications”. A Stanford group recently presented… Read More
Podcast EP81: The Future of Neural Processing with Quadric’s Steve Roddy
Dan is joined by Steve Roddy, chief marketing officer of Quadric, a leading processor technology intellectual property (IP) licensor. Roddy brings more than 30 years of marketing and product management expertise across the machine learning (ML), neural network processor (NPU), microprocessor, digital signal processor
Webinar: AMS, RF and Digital Full Custom IC Designs need Circuit Sizing
My career started out by designing DRAM circuits at Intel, and we manually sized every transistor in the entire design to get the optimum performance, power and area. Yes, it was time consuming, required lots of SPICE iterations and was a bit error prone. Thank goodness times have changed, and circuit designers can work smarter … Read More
