Disturbance In Verification


When writing my recent story about agentic verification, there was one quote from Abhi Kolpekwar, senior vice-president and general manager at Siemens EDA, that really struck a chord. He was talking about the additional token costs that would be consumed when a verification engineer starts asking the agents to do what was considered to be part of their job. "Consider the total cost of owners... » read more

Hardware From Specifications Using AI


There is a lot of excitement these days surrounding the idea that AI could make it possible to go from a specification to a design with absolutely no hardware skills. Well, get in line, because this is the umpteenth potential technology that was going to make that possible. Don't get me wrong, it just might do it, but will this be an implementation that is reliable, have decent performance, ... » read more

All Software Is Hardware-Dependent


I was lucky in my early career that I found two sets of great mentors. The first happened recently after graduating when I joined the Hilo development team. Members of that team included Phil Moorby, Simon Davdimann, Peter Flake, and others. They all had very different coding personalities, but most importantly, they worked as a team and used good foundational processes. One outcome of that ... » read more

Follow The AI Leader


In the 1980s, a common expression was "nobody ever got fired for buying IBM." It was considered the safe option, long after new technologies had emerged. While it may not have been the most advanced option available, it remained the safe bet. It had an established ecosystem, and it was a known quantity. But who or what is the safe bet when it comes to AI? Who has the necessary data? Who has ... » read more

The Verification Conundrum


When constrained random test pattern generation became the de facto way to verify designs, reference models became necessary to check that a design was producing the correct output. These were often distributed across several models, such as checkers, scoreboards and assertions. Another model that had to be created was the coverage model. It was required because you had to know if a generate... » read more

Tracking Your Preferences


I like to use my last blog of the year to focus on you, the reader. You provide valuable feedback to me and the rest of the team at Semiconductor Engineering. What do you want to see us write about? How in-depth should things be? This is always a balance between the amount of information provided and the rate at which readers tire with an article. My focus is the channels I write for – Sys... » read more

Spray And Pray Wastes Power


For quite some time I have felt that the way the industry approaches power is less than optimal. Techniques such as clock gating and power gating have been used to reduce the amount of unnecessary activity and leakage, but is there more activity that does not contribute to an intended action? While unnecessary activity may be unimportant in the functional sense, it all represents power that ... » read more

The Next Big Thing


Sometimes, we spend so much time looking for the next big thing that we actually miss something even bigger. I have to admit I was guilty of this while employed by a large EDA company 20 years ago. I was one of those ESL people — Electronic System Level acolytes, with Gary Smith as our standard bearer. We wanted to do many things, including raising the level of abstraction for design and veri... » read more

What Does Semiconductor Disruption Look Like?


When conducting interviews for my article on the incorporation of AI within EDA tools, Anand Thiruvengadam, senior director and head of AI product management at Synopsys, said, "AI has the potential to transform how customers do chip design. The entire EDA flow can be disrupted with AI." He is not alone in making this kind of statement. Each year, I do a predictions piece, and I ask about how A... » read more

AI Effort And Money Misplaced


While it is early days, and innovation is important, hyperscalers cannot afford to keep throwing money away forever. They need to work out how AI will earn money, and that relies on inference. For some time, I have been intrigued by the amount of money being spent on model development and AI training compared to the investment in inference. Models are an enabler, and every new model is attem... » read more

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