The most 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 alongside engineers as they design new computer chips. The problem it is trying to solve is one the industry has lived with for decades: chip design is enormously complex, ruinously expensive, and slow. Advanced chips take three to five years to go from conception to mass production; the design phase alone can take as long as two years before physical layout begins. Consider that the latest line of Nvidia GPUs, 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 all that investment a waste. Aalaei’s goal is to bring the kind of AI tools that software engineers have used to speed their work into the semiconductor design space.
“These systems have now become intelligent enough that by just guiding them and telling them what the result is that you want, it can actually produce beautiful code,” Aalaei told TechCrunch.
He says the firm’s technology can reduce the cost of chip development by more than 75% and cut the timeline by more than half.
The company emerged from stealth last year and said Wednesday that it had raised $60 million in new funding led by Seligman Ventures, with notable participation from Intel CEO Lip-Bu Tan, who invested through his venture firm Walden Catalyst Ventures and will be joining Cognichip’s board. Umesh Padval, a managing partner at Seligman, will also join the board. Cognichip has now raised $93 million altogether since its founding in 2024.
Still, Cognichip can’t yet point to a new chip designed with its system and did not disclose any of the customers it says it has been collaborating with since September.
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The company says its advantage is in using its own model trained on chip design data, rather than starting with a general-purpose LLM. That required getting access to domain-specific training data, which is no small feat. Unlike software developers, who share vast amounts of code openly, chip designers guard their IP closely, making the kind of open-source trove that typically trains AI coding assistants largely unavailable.
Cognichip has had to develop its own data sets, including synthetic data, and license data from partners. The firm has also developed procedures to allow chipmakers to securely train Cognichip’s models on their own proprietary data without exposing it.
Where proprietary data isn’t available, Cognichip has leaned on open-source alternatives. In one demo last year, Cognichip invited electrical engineering students at San Jose State University to try the model in a hackathon. The teams were able to use the model to design CPUs based on the RISC-V open-source chip architecture — a freely available design that anyone can build on.
Cognichip is competing against incumbent players like Synopsys and Cadence Design Systems, as well as a well-funded startups like ChipAgentsAI, which closed a $74 million extended Series A in February, and Ricursive, which raised a $300 million Series A round in January.
Padval said that the current flood of capital into AI infrastructure is the largest he’s seen in 40 years of investing.
“If it’s a super cycle for semiconductors and hardware, it’s a super cycle for companies like [Cognichip],” he said.
