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The most advanced silicon chips have accelerated the development of artificial intelligence. Now can AI return the favor?<\/p>\n
Cognichip<\/a> 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 \u2014 that\u2019s a lot to line up.<\/p>\n 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\u2019s goal is to bring the kind of AI tools that software engineers have used to speed their work into the semiconductor design space.\u00a0<\/p>\n \u201cThese 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,\u201d Aalaei told TechCrunch.<\/p>\n He says the firm\u2019s technology can reduce the cost of chip development by more than 75% and cut the timeline by more than half.\u00a0<\/p>\n 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 will be joining Cognichip\u2019s 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.<\/p>\n Still, Cognichip can\u2019t 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.\u00a0<\/p>\n Techcrunch event<\/p>\n \n\t\t\t\t\t\t\t\t\tSan Francisco, CA<\/span> 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.<\/p>\n Cognichip has had to develop its own datasets, including synthetic data, and license data from partners. The firm has also developed procedures to allow chipmakers to securely train Cognichip\u2019s models on their own proprietary data without exposing it.<\/p>\n Where proprietary data isn\u2019t 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 \u2014 a freely available design that anyone can build on.<\/p>\n Cognichip is competing against incumbent players like Synopsys and Cadence Design Systems, as well as well-funded startups like ChipAgents, which closed a $74 million extended Series A in February, and Ricursive, which raised a $300 million Series A<\/a> round in January.<\/p>\n Padval said that the current flood of capital into AI infrastructure is the largest he\u2019s seen in 40 years of investing.<\/p>\n \u201cIf it\u2019s a super cycle for semiconductors and hardware, it\u2019s a super cycle for companies like [Cognichip],\u201d he said. <\/p>\n<\/div>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\tOctober 13-15, 2026<\/span>\n\t\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n