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Artificial Intelligence
Artificial intelligence (AI) is transforming many aspects of our lives, from the way we work and communicate to the way we shop and travel. Its impact is felt in nearly every industry, including the semiconductor industry, which plays a crucial role in enabling the development of AI technology.
One of the ways AI is affecting our… Read More
AI is everywhere or so it seems, though often promoted with insufficient detail to understand methods. I now look for substance, not trade secrets but how exactly they using AI. Matt Graham (Product Engineering Group Director at Cadence) gave a good and substantive tutorial pitch at DVCon, with real examples of goal-centric optimization… Read More
Semiconductor technology is among the most complex of technologies and the semiconductor industry is among the most demanding of industries. Yet the ecosystem has delivered incredible advances over the last six decades from which the world has benefitted tremendously. Yes, of course, the markets want that break-neck speed… Read More
Artificial intelligence (AI) is a term used to describe machines that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI is classified into three main types: Narrow AI, General AI, and Super AI. Each type of AI has its unique… Read More
While I usually talk about AI inference on edge devices, for ADAS or the IoT, in this blog I want to talk about inference in the cloud or an on-premises datacenter (I’ll use “cloud” below as a shorthand to cover both possibilities). Inference throughput in the cloud is much higher today than at the edge. Think about support in financial… Read More
Edge AI inference is getting more and more attention as demand grows for AI processing across an increasing number of diverse applications, including those requiring low-power chips in a wide range of consumer and enterprise-class devices. Much of the focus has been on optimizing the neural network processing engine for these… Read More
Neural network models are advancing rapidly and becoming more complex. Application developers using these new models need faster AI inference but typically can’t afford more power, space, or cooling. Researchers have put forth various strategies in efforts to wring out more performance from AI inference architectures,… Read More
We looked at another paper on ML-based coverage acceleration back in April 2022. Here is a different angle from IBM. Paul Cunningham (Senior VP/GM, Verification at Cadence), Raúl Camposano (Silicon Catalyst, entrepreneur, former Synopsys CTO and now Silvaco CTO) and I continue our series on research ideas. As always, feedback… Read More
Often, AI inference brings to mind more complex applications hungry for more processing power. At the other end of the spectrum, applications like home appliances and doorbell cameras can offer limited AI-enabled features but must be narrowly scoped to keep costs to a minimum. New area-optimized AI inference technology from… Read More
I talked recently with Stelios Diamantidis (Distinguished Architect, Head of Strategy, Autonomous Design Solutions) about Synopsys’ announcement on the 100th customer tapeout using their DSO.ai solution. My concern on AI-related articles is in avoiding the hype that surrounds AI in general, and conversely the skepticism… Read More
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Quantum Simulation Using Decision Diagrams. Innovation in Verification