Cognichip raises $60M to use AI for designing the chips that power AI itself

Cognichip wants AI to design the chips that power AI, and just raised $60M to try | TechCrunch

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. 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 crop of well-funded startups. Among them: Alpha Design AI, which raised $21 million Series A in October 2025, and ChipAgentsAI, which closed a $74 million extended Series A in February. 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.

Cognichip raises $60 to reinvent chip design with physics-inspired AI models - SiliconANGLE

Cognichip raises $60 to reinvent chip design with physics-inspired AI models A startup called Cognichip said today it has raised $60 million in funding to try and accelerate momentum for the emerging concept of physics-informed chip design powered by advanced artificial intelligence models The round was led by Seligman Ventures and saw participation from Mayfield, Lux Capital, FPV and Candou Ventures, plus Intel Chief Executive Lip-Bu Tan, who will join its board of directors.. Cognichip argues that the semiconductor industry is approaching structural limits as the design of advanced chips becomes more expensive and time-consuming than ever before, requiring years of effort and hundreds of millions of dollars. As a result, the advance of AI itself is slowing, as the chips fail to keep up with the capabilities of more powerful models. The startup has not built another electronic design automation tool. Instead, it's aiming to rethink the entire way that chips are designed with its Artificial Chip Intelligence design platform. ACI is a physics-informed foundational model that's built specifically for chip design. Unlike general-purpose models, it integrates elements such as physical constraints, circuit behavior and the difficulties of manufacturing into the semiconductor design process. This enables it to reason across every step, from the architecture design to verification and even production. Cognichip argues that traditional chip design processes are too sequential, with engineers moving step-by-step through each workflow. Instead of doing this, the startup's approach embraces parallelism, which means that multiple design decisions can be explored simultaneously. That's important because the most advanced chips today span digital, analog and mixed-signal domains, and each part has interdependencies with the other, making optimization extremely difficult. But by embedding physics directly into its foundational model, Cognichip can calculate all of these tradeoffs in ways that data-driven EDA tools cannot. The startup says this means ACI acts more like an engineering collaborator rather than a design tool, solving problems through advanced reasoning. As a result, Cognichip says it's able to reduce the effort that goes into chip design by up to 50%. Cognichip says it's currently working with more than 30 semiconductor design companies, including some of the industry's biggest players, and says its platform is now being tested in real-world production workflows. Early adopters report reductions in chip design cycles and costs, along with enhanced performance. The platform also allows chipmakers to maintain their existing manufacturing standards, which is critical for its broader adoption. However, Cognichip declined to name any of its customers, and did not mention any specific chips that its platform has helped to design. "The semiconductor industry is at a critical juncture; an AI framework for innovation and efficiency will unlock massive global opportunity," said Tan. "Success in this space requires a rare fusion of deep domain expertise combined with advanced AI research and an end-to-end integrated design approach. Cognichip's groundbreaking, physics-informed foundation model technology and proven leadership team position it to become a generational company." Cognichip's rise comes at an interesting juncture, where it seems that there's a growing dependency between AI and the hardware that powers it. Many AI models are reaching performance limits and require more powerful processors, but those chips can take years to design, curtailing progress in the industry. If it succeeds in compressing design timelines into a matter of months instead of years, Cognichip will not only accelerate chip innovation, but potentially boost the momentum of the entire AI ecosystem. "The next wave of progress to significantly reduce chip design cycles will not come from incremental optimization of existing design tools, but from using AI to parallelize what has historically been a highly serial chip design process," said Seligman Managing Partner Umesh Padval. "Cognichip is building the foundation for that shift through physics-informed models, curated datasets, and production-ready integration with the semiconductor design stack."