Cognichip emerges from stealth, with $33 million to launch “artificial chip intelligence” and reshape semiconductor designs

In a bold leap in semiconductor technology, Cognichip launched from stealth with $33 million in seed funding to build so-called artificial chip intelligence (ACI®), a fundamental shift in chip design, development and launching to the market. The funding round is led by Lux Capital and Mayfield, from FPV and Candou Ventures.

The San Francisco-based startup targets two of the biggest hurdles in chip design: super cost and time. With the development cycle usually exceeding 3-5 years and $100 million per chip, innovation in semiconductor space has slowed down significantly. Founded by industry veteran Faraj Aalaei, who previously revealed two semiconductor companies and served as CEO of Centillium Communications, Cognichip plans to change that.

What is artificial chip intelligence (ACI®)?

At the heart of the Cognichip platform is the AI ​​fundamental model for physical knowledge of semiconductor design, which is sharply different from traditional tools and processes. Called ACI®, this new system introduces AI to “designer-level cognitive abilities”, allowing it to understand, learn and optimize the entire chip development process with human-like reasoning and physical consciousness.

This model is not only an automated workflow, it can also redefine them. By embedding AI into the physics of semiconductor systems, ACI® can simultaneously analyze global and local variables, design components in parallel, and perform constraint-aware optimizations throughout the chip stack. this Session design method Replaced the rigid serial process that has restricted the industry for decades.

Key performance goals of ACI® include:

• 50% reduction in development time: Thanks to the parallel AI-driven design cycle
• 75% reduction in cost: By minimizing engineering labor and testing redundancy
• Smaller, more efficient chips: Optimize power, performance and region (PPA) metrics in real time
• Greater adaptability: ACI® can quickly design changes and support smaller, more professional chips

Why is it important now

Despite the exponential rise in AI, semiconductor innovation is still lagging behind. While generated AI models can be deployed in a few weeks, it still takes years to design the chips they run. This progress in disconnecting bottleneck hardware has prevented new entrants.

Cognichip faces this topic head-on. Its technology allows engineers to focus on innovation rather than infrastructure, allowing anyone in major businesses to bring new chips to the market – faster, cheaper, and requires less expertise.

Faraj AalaeiCEO and founder, explained:

“Even during the AI ​​boom, semiconductor startups are still scarce – only about eight VC-powered chip startups appear every year today, compared to 2000 in 2000. It’s not because of the lack of ideas, it’s because the system is broken. With ACI®, we rewrite the rules.”

Veteran team, modern mission

Cognichip’s founding team is a celebrity for AI and semiconductor veterans:

• Ehsan Kamalinejadco-founder and CTO: LED Apple’s AI capabilities (such as photo memories) and AWS’s groundbreaking enhanced learning
• Simon SabatoCo-founder and Chief Architect: Former Chief Architect of Google, Cisco and Cadence
• Mehdi DaneshpanahVice President of Software: KLA’s global software front
• stelios diamantidisChief Product Officer: The creator of Synopsys AI-driven DSO.AI platform

Supporting them are PhDs from MIT, Stanford, Berkeley and the University of Toronto, as well as Olympic medalists in mathematics and physics. This interdisciplinary team is building potentially becoming the world’s first true cognitive engine for CHIP creation.

From bottleneck to breakthrough

Cognichip is not only aimed at improving chip design, but also aimed at democratizing it. With AI dealing with most complexities, small startups and research teams can quickly design the leverage chips that were previously reserved for billions of dollars.

This pair:

• Artificial Intelligence Infrastructuremore and more customized accelerators are needed
• Health carewhich requires low-power, high-efficiency chips for wearable devices and diagnostics
• vitalityOptimization of calculation per watt is a critical task
• Autonomous systemThis requires large-scale domain-specific silicon

Investors think it’s not just a better chip to bet – they think it’s a transformation Innovation Stack For the entire technology ecosystem.

“This is not a tool – it is a paradigm shift,” explain Navin ChaddhaManaging Partner of Mayfield. “Cognichip’sAci® replaces brute force design with intelligent, AI-powered creation. This is the future.”

The road ahead: AI chips, reshaping

The semiconductor industry is at a critical crossroads. As generated AI systems drive the limitations of computing demand, there is an increasing consensus that traditional chip design approaches can no longer keep pace. Major tech companies are now competing to develop AI-regulated chips – from inference-optimized accelerators to domain-specific processors for edge computing, robotics and energy-efficient data centers.

However, the bottleneck is not in manufacturing, but in design. Developing these new chips still requires years of engineering work, substantial capital investment and deep field expertise, which remove all obstacles except the biggest players. This mismatch between the speed of AI model development and chip design is creating a widening gap in the innovation stack.

Cognichip’s vision is to close the gap. By introducing ACI®, the company is laying the foundation for not only the new era of AI consuming computing, but also actively promoting the creation of it. This shift could enhance the new wave of hardware innovation, unlock faster, cheaper, and more tailored chips, from personalized medical devices to next-generation autonomous systems.

As the industry moves towards the edge of trillion-parameter models and real-time AI, the demand for agile, optimized, privacy-conscious chips will only accelerate. Cognichip positioned itself at the center of this transformation—not by making the chip faster, but by making the chip creation itself smart, easy to access and exponentially more scalable.

In this new paradigm, the difference between software and hardware blur and the most important breakthrough may come from not only the new algorithms, but also the machines that design machines.