Autonomous Semiconductor Design Using Reinforcement Learning: Toward AI-Native Chip Engineering

Authors: Arnav Butail

Abstract: The rapid advancement of integrated circuits has led to designs containing hundreds of billions of transistors. Additional months of effort are required for physical design even with advanced Automated Design Tools. This paper assesses the gap closing potential of reinforcement learning, especially as implemented by DeepMind’s proposed AlphaChip, and presents the theory of RL-based design, analyzes the architecture of AlphaChip and Edge-based Graph Neural Networks, evaluates deployment results for several generations of Google’s TPUs, and analyzes the results within the scope of AI-based EDA tools by Synopsys, Cadence, and academia. Finally, the limitations are evaluated, and within the next decade, an Agentic EDA, where fully autonomous Agents optimize the entire physical design flow, is proposed. Based on the scope of today’s technology, we state the true value of AlphaChip design is the ability to perform a task that, to this point, could solely be done with the aid of a computer, in a manner that can partially be taught.

DOI: http://doi.org/10.5281/zenodo.20731145