Editor-Curated Robotics Industry Briefing - RobotToday

AI Is Designing Radio Chips That Humans Couldn’t Even Imagine

Researchers at Princeton University have made significant strides in the design of radio-frequency integrated circuits (RFICs), a critical component for advancing wireless technologies such as 5G, autonomous vehicles, and satellite communications. Utilizing reinforcement learning and inverse design techniques, the team has developed a method to create RFICs from scratch, drastically reducing design time and achieving record performance levels. This innovative approach leverages AI to navigate the complex design space of RFICs, traditionally seen as an art requiring years of expertise. By employing machine learning algorithms, the researchers can generate novel circuit layouts that outperform existing designs while minimizing the time taken for development. The project, which began after the success of AI in games like Go, aims to overcome the limitations of conventional RFIC design, which has remained largely artisanal. The researchers emphasize the need for large, shared datasets and open ecosystems to further enhance AI's capabilities in understanding electromagnetic and circuit behaviors. As the demand for advanced RFICs grows, the potential for AI-driven design to revolutionize the field is becoming increasingly apparent. The findings have attracted attention within the RF community, sparking discussions about the future of AI in circuit design and the importance of collaboration between AI researchers and chip designers to unlock new possibilities in technology.

IEEESpectrumAI By Kaushik Sengupta 6 hours ago Machine-learning Ic-design Chip-design Rf Rfic

AI Agent Designs a RISC-V CPU Core From Scratch

In a significant advancement for AI-driven chip design, Verkor.io, an AI chip design startup, has successfully created a RISC-V CPU core entirely through an autonomous AI system named Design Conductor. This milestone was achieved in December 2025, with the resulting CPU, dubbed VerCore, boasting a clock speed of 1.5 GHz and performance comparable to a 2011 laptop CPU. Suresh Krishna, co-founder of Verkor.io, emphasized that their approach, which allows the AI to tackle the entire design process rather than just specialized tasks, is more effective. Design Conductor operates as a structured harness for large language models (LLMs), guiding the AI through a series of steps akin to those followed by human engineers, from design to testing. The system autonomously generated the VerCore design in just 12 hours based on a 219-word specification. While VerCore has not yet been physically produced, it has been verified through simulation, achieving a score of 3,261 on the CoreMark benchmark. Verkor.io plans to release the design files for VerCore and other projects by the end of April and will showcase an FPGA implementation at the upcoming DAC conference. Despite the potential of AI in chip design, experts caution that human intuition remains crucial, as AI systems can struggle with complex design challenges. While Design Conductor may streamline the design process, it is not yet capable of replacing human engineers entirely, requiring a team of experts to achieve production-ready designs.