LOFMPL: An Open-source Logic Optimization Framework with MFFC-based Hypergraph Partition and Reinforcement Learning for Large Circuits

Abstract

LOFMPL is an open-source framework designed for advanced logic optimization, specifically targeting the challenges presented by extremely large integrated circuits. It utilizes a scalable, two-pronged optimization approach, starting with Maximal Fanout-Free Cone (MFFC)-based hypergraph partitioning for efficient circuit decomposition. Crucially, the framework integrates Reinforcement Learning (RL) to intelligently guide the optimization policy, resulting in superior quality-of-results compared to traditional heuristic methods.

Report

LOFMPL Framework Analysis

Key Highlights

• Hybrid Optimization Strategy: LOFMPL combines traditional, robust structural decomposition (MFFC Hypergraph Partitioning) with cutting-edge AI techniques (Reinforcement Learning) to find high-quality solutions for complex optimization problems.
• Scalability Focus: The framework is explicitly designed to handle "Large Circuits," addressing the massive scale of modern VLSI designs that often overwhelm conventional tools.
• Open-Source Commitment: Being open-source, LOFMPL provides transparent, auditable, and freely available high-performance optimization capabilities, democratizing access to advanced EDA methods.
• Improved Decision Making: The use of RL allows the optimization flow to move beyond rigid, pre-programmed heuristics, learning optimal sequence and parameter choices based on real-time circuit state feedback.

Technical Details

• Architecture Name: LOFMPL (Logic Optimization Framework).
• Partitioning Method: Maximal Fanout-Free Cone (MFFC)-based Hypergraph Partition. MFFCs are used to identify isolated sub-networks that minimize connectivity to the rest of the circuit, allowing for parallel and independent local optimization, which is essential for managing complexity.
• Optimization Driver: Reinforcement Learning (RL). RL agents are employed to dynamically determine the most effective logic transformations (e.g., rewriting, restructuring, balancing) at specific stages of the optimization flow, maximizing improvements in area, delay, or power metrics.
• Target Scope: Synthesis flows, specifically targeting technology-independent and technology-dependent optimization stages where performance gains are critical.

Implications

• Advancing Open-Source EDA: The release of a high-performance, AI-accelerated optimization framework significantly strengthens the open-source Electronic Design Automation (EDA) ecosystem, which is crucial for fostering independent hardware development.
• Benefits for RISC-V Development: The RISC-V movement heavily relies on open-source tools. LOFMPL provides core designers with a powerful, accessible tool to optimize the generated netlists of RISC-V cores, directly leading to smaller, faster, and more power-efficient hardware implementations.
• Lowering Barriers to Entry: By providing an open alternative to proprietary optimization tools, LOFMPL enables academic researchers, startups, and smaller design teams to tackle state-of-the-art chip complexity without substantial licensing costs.
• Validation of AI in Design Flow: This work reinforces the trend of integrating sophisticated machine learning, like RL, into core EDA functions, paving the way for fully autonomous and highly optimized physical design flows in future VLSI generations.