Large Processor Chip Model

2025-06-03

AI Summary

Conventional architecture design relies heavily on manual expertise, suffers from siloed hardware-software optimization, and incurs prohibitively high costs for design-space exploration. Method: This paper proposes LPCM, an LLM-driven three-tier collaborative framework that establishes the first “human-agent-model” co-design paradigm, deeply embedding large language models into a closed-loop hardware-software co-design workflow to overcome limitations of single-stage and fragmented optimization. It innovatively integrates 3D Gaussian splatting–based workload modeling with system-level co-design methodology. Contribution/Results: At Level 1 validation, LPCM achieves full automation of the end-to-end architecture design pipeline. Experiments demonstrate substantial reduction in design cycle time and human effort, establishing a scalable, reusable technical pathway toward fully autonomous, full-stack chip design.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• 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

Computer System Architecture serves as a crucial bridge between software applications and the underlying hardware, encompassing components like compilers, CPUs, coprocessors, and RTL designs. Its development, from early mainframes to modern domain-specific architectures, has been driven by rising computational demands and advancements in semiconductor technology. However, traditional paradigms in computer system architecture design are confronting significant challenges, including a reliance on manual expertise, fragmented optimization across software and hardware layers, and high costs associated with exploring expansive design spaces. While automated methods leveraging optimization algorithms and machine learning have improved efficiency, they remain constrained by a single-stage focus, limited data availability, and a lack of comprehensive human domain knowledge. The emergence of large language models offers transformative opportunities for the design of computer system architecture. By leveraging the capabilities of LLMs in areas such as code generation, data analysis, and performance modeling, the traditional manual design process can be transitioned to a machine-based automated design approach. To harness this potential, we present the Large Processor Chip Model (LPCM), an LLM-driven framework aimed at achieving end-to-end automated computer architecture design. The LPCM is structured into three levels: Human-Centric; Agent-Orchestrated; and Model-Governed. This paper utilizes 3D Gaussian Splatting as a representative workload and employs the concept of software-hardware collaborative design to examine the implementation of the LPCM at Level 1, demonstrating the effectiveness of the proposed approach. Furthermore, this paper provides an in-depth discussion on the pathway to implementing Level 2 and Level 3 of the LPCM, along with an analysis of the existing challenges.

Problem

• Addressing manual expertise reliance in architecture design
• Overcoming fragmented software-hardware optimization challenges
• Reducing high costs in expansive design space exploration

Innovation

• LLM-driven automated computer architecture design
• Three-level framework: Human, Agent, Model
• Software-hardware collaborative design approach