The increasing demands of data-intensive applications necessitate more efficient storage and memory utilization. The rapid evolution of AI workloads, particularly with Generative AI (GenAI), demands infrastructure that can adapt to diverse computational needs. AI models vary widely in resource requirements, necessitating… Read More
Our workshop will feature content including, but not limited to, the following areas:
Join us on LinkedIn Live to discuss “The Rise of the DPU”! Dive deep into Data Processing Units (DPUs) with industry experts Ron Renwick (Achronix), Patrick Kennedy (Servethehome.com), Baron Fung (Dell’Oro), and Scott Schweitzer (Achronix). Join our conversation to discuss technological advancements,… Read More
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
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
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
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 (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
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
“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
