Ethernovia, a Silicon Valley-based semiconductor startup, has closed a Series B funding round exceeding $90 million, led by Maverick Silicon with participation from Socratic Partners, Conduit Capital, and CDIB-TEN Capital. Existing investors, including Porsche SE, Qualcomm Ventures, and Fall Line Capital, also contributed to the round. The funding aims to accelerate the development of Ethernovia’s next-generation networking chips designed for real-time sensor and AI data processing in autonomous systems.
Driving the Next Era of Physical AI
The company is pioneering a class of high-performance automotive and edge packet processors that serve as the backbone for physical AI, a term describing systems capable of sensing, reasoning, and acting in the real world with deterministic precision. Ethernovia’s platform enables Ethernet-based, software-defined networks that can process and orchestrate massive volumes of real-time sensor and AI traffic. This innovation addresses the limitations of legacy networks, which were not designed to handle AI-driven workloads in vehicles, robotics, and industrial applications.
A New Class of Packet Processors
At the core of Ethernovia’s platform are first-of-their-kind packet processors engineered to aggregate, route, and manage high-bandwidth sensor, vision, and AI data streams with industry-leading power efficiency. These processors provide the deterministic performance necessary for autonomous driving, advanced driver assistance systems (ADAS), robotic perception, and other AI-defined applications. By reducing system complexity, weight, and cost, the technology simplifies the design of next-generation intelligent machines while enabling scalable, centralized, and zonal architectures.
Flexibility and Edge AI Capabilities
Designed for edge and physical AI workloads, Ethernovia’s processors support programmable data paths and scalable Ethernet architectures that span vehicles, robotics platforms, and mobility systems. This flexibility allows OEMs and developers to deploy software-defined systems that can evolve through over-the-air updates without compromising safety-critical performance. As autonomous systems grow increasingly complex, the platform’s ability to maintain low latency and high efficiency is becoming a critical differentiator.
Investor Confidence in a Growing Market
Kenneth Safar, managing director of Maverick Silicon, emphasized the strategic importance of Ethernovia’s technology, noting that latency, power efficiency, and architectural flexibility are essential for modern autonomous systems. Socratic Partners’ founding partner Rick Clemmer added that Ethernovia addresses one of the most pressing infrastructure challenges for physical and edge AI today, enabling OEMs to simplify system design while supporting scalable, software-defined networks. The strong investor interest highlights growing demand for solutions that underpin the next generation of autonomous vehicles, robotics, and industrial AI applications.
Plans for Growth and Market Expansion
Ethernovia intends to use the Series B funding to accelerate the production of its advanced packet processors and expand its software and systems capabilities. The company also plans to enhance customer engagements across automotive, robotics, and industrial markets, ensuring broad adoption of its technology. This expansion aligns with Ethernovia’s mission to redefine the “nervous system” of intelligent machines, delivering the data infrastructure necessary for high-performance, AI-driven operations at the edge.
With its innovative packet processor platform and robust investor backing, Ethernovia is positioning itself at the forefront of physical AI and autonomous system infrastructure. By enabling real-time, reliable data management across complex systems, the company is helping OEMs and developers overcome critical networking bottlenecks. As demand for intelligent, software-defined machines grows, Ethernovia’s technology is set to become a foundational component of next-generation autonomous vehicles, robotics, and industrial AI platforms.