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Crack the Verification Double Trouble!
Register for CadenceTECHTALK to find out how to achieve verification closure with the same coverage with up to a 10X reduction in simulation cycles.
Chips are becoming bigger and more complex, adding to already existing verification woes. Design and verification engineers struggle with… 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
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
tinyML Auto ML Forumby Admin on 06-06-2022 at 12:39 pm
Educating end users on what you can do with tinyAutoML
About
Tiny machine learning is broadly defined as a fast-growing field of machine learning technologies and applications including hardware and software capable of performing on-device sensor data analytics at extremely low power. The tinyML Foundation is powering the
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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
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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
I recall meeting with Solido at DAC back in 2009, learning about their Variation Designer tool that allowed circuit designers to quickly find out how their designs performed under the effects of process variation, in effect finding the true corners of the process. Under the hood the Solido tool was using Machine Learning (ML) techniques… Read More
