Pushing the Limits of Machine Design: Automated CPU Design with AI
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Abstract (arXiv)
Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abilities in designing new materials, proteins, and computer programs with advanced artificial intelligence (AI) techniques, the search space for designing such objects is relatively small, and thus, "Can machines design like humans?" remains an open question. To explore the boundary of machine design, here we present a new AI approach to automatically design a central processing unit (CPU), the brain of a computer, and one of the world's most intricate devices humanity have ever designed. This approach generates the circuit logic, which is represented by a graph structure called Binary Speculation Diagram (BSD), of the CPU design from only external input-output observations instead of formal program code. During the generation of BSD, Monte Carlo-based expansion and the distance of Boolean functions are used to guarantee accuracy and efficiency, respectively. By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours. The taped-out CPU successfully runs the Linux operating system and performs comparably against the human-designed Intel 80486SX CPU. In addition to learning the world's first CPU only from input-output observations, which may reform the semiconductor industry by significantly reducing the design cycle, our approach even autonomously discovers human knowledge of the von Neumann architecture.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 90 references that are in the map. First posted on arXiv 21 Jun 2023.
Builds on
9 of 90 references.
References · 9
- 2022Competition-Level Code Generation with AlphaCodecited by 1,462
- 2020Drug discovery with explainable artificial intelligencecited by 118
- 2021Machine Learning for Electronic Design Automation: A Surveycited by 72
- 2021Learning to Synthesize Programs as Interpretable and Generalizable Policiescited by 59
- 2020Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discoverycited by 24
- 2019Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Designcited by 17
- 2019Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesiscited by 8
- 2020Logic Synthesis Meets Machine Learning: Trading Exactness for Generalizationcited by 8
- 2020Improving Performance Estimation for FPGA-based Accelerators for Convolutional Neural Networkscited by 5
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.