AGON: Automated Design Framework for Customizing Processors from ISA Documents

2024-12-30

AI Summary

Custom processor design suffers from low efficiency, a disconnect between automation and real-world performance, and particular difficulty in rapidly generating high-performance out-of-order (OoO) execution architectures. Method: This paper proposes the first domain-specific processor auto-design framework powered by large language models (LLMs). It takes natural-language instruction-set documentation as input and introduces a novel nanoscale operator intermediate representation (nOP-IR) that decouples functional semantics from PPA (performance, power, area) optimization—enabling expressive, formally correct, and automatically optimized hardware generation. The framework integrates LLM-driven RTL synthesis, nOP-IR compilation, formal semantic verification, and PPA-aware logic synthesis. Results: Evaluated on OoO processors, our generated designs achieve 2.35× average speedup over expert-designed BOOM cores, while drastically reducing design cycle time and human effort.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Customized processors are attractive solutions for vast domain-specific applications due to their high energy efficiency. However, designing a processor in traditional flows is time-consuming and expensive. To address this, researchers have explored methods including the use of agile development tools like Chisel or SpinalHDL, high-level synthesis (HLS) from programming languages like C or SystemC, and more recently, leveraging large language models (LLMs) to generate hardware description language (HDL) code from natural language descriptions. However, each method has limitations in terms of expressiveness, correctness, and performance, leading to a persistent contradiction between the level of automation and the effectiveness of the design. Overall, how to automatically design highly efficient and practical processors with minimal human effort remains a challenge. In this paper, we propose AGON, a novel framework designed to leverage LLMs for the efficient design of out-of-order (OoO) customized processors with minimal human effort. Central to AGON is the nano-operator function (nOP function) based Intermediate Representation (IR), which bridges high-level descriptions and hardware implementations while decoupling functionality from performance optimization, thereby providing an automatic design framework that is expressive and efficient, has correctness guarantees, and enables PPA (Power, Performance, and Area) optimization. Experimental results show that superior to previous LLM-assisted automatic design flows, AGON facilitates designing a series of customized OoO processors that achieve on average 2.35 $ imes$ speedup compared with BOOM, a general-purpose CPU designed by experts, with minimal design effort.

Problem

• Custom Processor Design
• Out-of-Order Execution
• Efficiency Optimization

Innovation

• nOP Function
• Customized Processor Design
• Performance Optimization