GoKawiilFor decades, semiconductor design has been driven by humans coming up with bright ideas that unlock new innovations. But the benefits of better chip design have been reaped, including the rise of AI, which now means there could be another party involved in making chip designs smarter: AI itself.
‘Chip designer’ isn’t one of the roles on the chopping block as AI automation upends the job market. But in the narrow pockets of the design flow where problems are structured, and evaluators are robust, it is starting to be adopted — with benefits.
Google DeepMind's AlphaChip reinforcement-learning system has produced designs for three generations of the company's Tensor Processing Units (TPUs), with DeepMind claiming "superhuman" layouts compared with those produced by human designers. They’re not alone: Synopsys has passed 100 production tape-outs with its DSO.ai design-space-optimization tool, reporting productivity boosts of more than three times and power reductions of up to 25% for customers including STMicroelectronics and SK hynix.
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"Like every new technology, AI may have multiple uses," said Borivoje Nikolić, professor of electrical engineering and computer sciences at the University of California, Berkeley, in an interview with Tom’s Hardware Premium. Nikolić drew a parallel with Moore's Law, which has historically been exploited in two ways: to reduce the cost of an existing product by porting it to cheaper processes, or to add features that were previously impossible. "I think AI will be used in both ways," he says. "At the moment, the industry seems to be focused on the first item — how to make things cheaper, how to automate things in a better way than they were in the past."
(Image credit: Bella Ciervo, Penn Engineering)
By contrast, academics are more interested in using AI to discover things humans haven't yet thought of, an approach that mirrors breakthroughs in areas such as drug discovery and protein folding with the likes of AlphaFold.
Nikolić and his colleague Sagar Karandikar have been exploring that territory in their own research on cache replacement policies, a subject deep in the weeds of processor microarchitecture. Their ArchAgent system, built on Google DeepMind's AlphaEvolve framework, generated a cache replacement policy in two days that beat the prior state-of-the-art by 5.3% in IPC speedup on Google's multi-core workload traces. On the heavily worked-over single-core SPEC06 benchmarks, it took 18 days to eke out another 0.9%. That’s a "first sign of life" for Karandikar that large language models can design genuinely new logic, rather than just tinkering with existing parameters.
"There is still a lot of human guidance, and it kind of up-levels the kind of thinking humans have to do," said Karandikar, a computer architecture researcher at Berkeley, in an interview with Tom’s Hardware Premium. "The humans involved in that project are doing more of the high-level thinking — coming up with new ideas and guiding the LLM — and the LLM does a lot of the finer policy development around that."
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