Pushing the Limits of Machine Design: Automated CPU Design with AI (2306.12456v2)

Abstract: 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 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.

• The paper presents a novel AI-driven approach that automates CPU design by inferring circuit logic from input-output examples using Binary Speculation Diagrams.
• It employs Monte Carlo-based policies and Boolean-distance metrics for node reduction, ensuring accuracy above 99.99999999999% while scaling complex designs.
• The methodology successfully generated a RISC-V CPU that booted Linux and matched the Intel 80486SX's performance, reducing design time to just five hours.

Automated CPU Design with AI

Introduction

The paper "Pushing the Limits of Machine Design: Automated CPU Design with AI" presents a novel AI-driven approach for the automated design of central processing units (CPUs) using input-output observations rather than formal program code. This method leverages Binary Speculation Diagrams (BSD) and Monte Carlo-based policies to generate industrial-grade CPU designs, functionally validated and compared against commercial processors, such as the Intel 80486SX. The following sections explore the methodologies, challenges, and implications of this research.

Learning Circuit Logic from Input-Output Examples

The traditional CPU design pipeline involves manually crafting formal programming representations of circuit logic, which are then optimized using electronic design automation (EDA) tools. However, the AI approach described in this paper proposes a paradigm shift, wherein the circuit logic is inferred from finite input-output examples through a computational structure called Binary Speculation Diagram (BSD).

Figure 1: CPU learning flow, circuit logic representation, and detailed learning process.

BSD acts as a compressed representation of the circuit logic, allowing for efficient computation while maintaining the accuracy required by the intricate designs of a CPU. The generation process of BSDs relies on speculative execution, whereby subtrees of a conventional Binary Decision Diagram (BDD) are replaced with speculative leaf nodes, simplifying the logic without losing correctness.

Addressing Accuracy and Scalability

The approach confronts two profound challenges: ensuring accuracy and managing scalability. To maintain high accuracy, the technique employs a Monte Carlo-based expansion policy. It iteratively evaluates whether the speculated outputs meet the required performance across known and unseen inputs, ensuring precision beyond 99.99999999999%.

Figure 2: One iteration of the proposed node-reduction policy.

Scalability is enhanced through a node-reduction policy using Boolean-distance metrics. This involves clustering similar nodes to allow merging, which reduces the complexity of the generated BSD without compromising accuracy, ultimately managing the problem's massive search space.

Real-World Application and Evaluation

The AI-driven CPU design methodology was practically evaluated by generating a RISC-V CPU, boasting remarkable efficiency and performance metrics. The CPU not only booted a Linux operating system but also matched the performance of an Intel 80486SX in benchmark tests, demonstrating the practical applicability of the BSD approach.

Figure 3: The layout, manufactured chip, and printed circuit board.

The CPU was manufactured with 65nm technology, designed within five hours—a dramatic reduction from the traditional cycle requiring extensive human expertise and time. This innovation substantiates the AI's potential to revolutionize semiconductor design by automating critical steps that previously demanded substantial manual intervention.

Discovering the von Neumann Architecture

Remarkably, the AI approach autonomously uncovers the von Neumann architecture, establishing key CPU modules without preexisting architectural knowledge. This discovery aligns with foundational CPU structures, including control and arithmetic units, further validating AI's capability in mirroring human architectural design principles.

Figure 4: Discovering the von Neumann architecture from scratch.

Conclusion

The research paves the way for groundbreaking advances in CPU design, reducing human dependency and the lengthy design cycle inherent in traditional processes. The AI-designed CPU's competitive performance and its autonomous discovery of established architectural principles highlight the technique's maturity and demonstrate a forward path toward more self-evolving, AI-driven design methodologies. Future explorations may yield novel architectural optimizations, offering untapped potentials in processor design.