More advanced silicon chips have accelerated the development of artificial intelligence. Now can AI return the favor?
Cognichip is building a deep learning model to work with engineers as they design new computer chips. The problem it’s trying to solve is one the industry has lived with for decades: chip design is extremely complex, prohibitively expensive and slow. Advanced chips take three to five years to go from concept to mass production. the design phase alone can take up to two years before the physical layout begins. Consider that Nvidia’s latest graphics line, Blackwell, contains 104 billion transistors — that’s a lot to line up.
In the time it takes to create a new chip, Cognichip CEO and founder Faraj Aalaei says the market can change and make that entire investment a waste. Aalaei’s goal is to bring the kind of artificial intelligence tools that software engineers have used to speed up their work to the semiconductor design space.
“These systems have now become smart enough that just by instructing them and telling them what the result is that you want, they can actually produce beautiful code,” Aalaei told TechCrunch.
He says the company’s technology can cut chip development costs by more than 75 percent and cut the timeline by more than half.
The company emerged from stealth last year and said Wednesday it had raised $60 million in new funding led by Seligman Ventures, with notable participation from Intel CEO Lip-Bu Tan, who will join Cognichip’s board. Umesh Padval, managing partner at Seligman, will also join the board. Cognichip has raised a total of $93 million since its founding in 2024.
However, Cognichip cannot yet indicate a new chip designed with its system, and has not disclosed any of the customers it says it has been working with since September.
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The company says its advantage is that it uses its own model trained on chip design data, rather than starting with a generic LLM. This required access to domain-specific training data, which is no small feat. Unlike software developers, who share vast amounts of code openly, chip designers closely guard their IP, making the kind of open source that typically trains AI coding assistants largely unavailable.
Cognichip had to develop its own data sets, including synthetic data and license data from partners. The company has also developed processes that allow chip makers to safely train Cognichip’s models on their own proprietary data without exposing it.
Where proprietary data is not available, Cognichip has relied on open source alternatives. In a demo last year, Cognichip invited electrical engineering students at San Jose State University to test the model at a hackathon. The teams were able to use the model to design CPUs based on the open-source RISC-V chip architecture — a freely available design that anyone can build on.
Cognichip competes with incumbents like Synopsys and Cadence Design Systems, as well as well-funded startups like ChipAgents, which closed a $74 million Series A expansion in February, and Recursive, which raised a $300 million Series A in January.
Padval said the current flood of capital into AI infrastructure is the largest he has seen in 40 years of investment.
“If it’s a super cycle for semiconductors and hardware, it’s a super cycle for companies like [Cognichip]”, he said.
