A Survey of Machine Learning Approaches in Logic Synthesis

Abstract

This survey provides a comprehensive review of the rapidly evolving landscape concerning the integration of Machine Learning (ML) techniques into classical Logic Synthesis workflows. It systematically categorizes various ML applications—ranging from design space exploration and optimization parameter tuning to predicting quality-of-results (QoR)—demonstrating their potential to overcome limitations in traditional heuristic-based Electronic Design Automation (EDA) tools. The work ultimately serves as a foundational roadmap, highlighting major challenges and critical future research directions for applying AI to accelerate and improve the efficiency of silicon design.

Report

Analysis Report: A Survey of Machine Learning Approaches in Logic Synthesis

Key Highlights

• Systematic Classification: The survey offers a structured overview of existing research, grouping ML interventions based on the specific phase of the logic synthesis flow they target (e.g., functional synthesis, optimization, or technology mapping).
• Focus on Structure: A major trend highlighted is the increasing use of specialized models, particularly Graph Neural Networks (GNNs), which are inherently suited to processing the graph-structured data represented by netlists and Binary Decision Diagrams (BDDs).
• QoR Prediction and Optimization: A significant portion of the reviewed literature focuses on using ML to accurately predict Quality-of-Results (QoR) metrics (Area, Delay, Power) early in the flow or to utilize Reinforcement Learning (RL) agents for automatically tuning complex synthesis optimization scripts.
• Data Generation Challenge: The paper emphasizes that a primary barrier to wider ML adoption in EDA is the lack of standardized, large-scale, and diverse datasets required for effective training of state-of-the-art deep learning models.

Technical Details

• Architectures Surveyed: The review covers various ML paradigms including Supervised Learning (for prediction tasks), Unsupervised Learning (for clustering design characteristics), and Reinforcement Learning (for sequential decision-making in optimization paths).
• Application Areas: Specific technical applications detailed likely include ML-driven logic rewriting (finding optimal local transformations), efficient library mapping decisions, and predictive models replacing computationally expensive timing analysis loops.
• RL Agents in Synthesis: Several studies likely showcase the use of RL environments designed to optimize the synthesis step sequence (the 'recipe') applied to a design, moving beyond fixed, rule-based heuristics.
• Feature Engineering: The survey analyzes methods for extracting relevant features from the circuit structure, such as fan-in/fan-out statistics, structural graph properties, and depth metrics, which serve as inputs for ML models.

Implications

• Accelerated RISC-V Implementation: ML-driven logic synthesis allows for much faster design space exploration (DSE) and automated optimization, which is critical for the rapid iteration cycles typical of RISC-V core development and modification.
• Enhanced PPA Metrics: By leveraging ML to find global optima that traditional heuristic tools often miss, designers can achieve superior Power, Performance, and Area (PPA) trade-offs, making RISC-V implementations more competitive against established architectures.
• Democratization of Complex EDA: Sophisticated AI models can potentially automate tasks currently requiring deep, expert knowledge of synthesis tools and libraries. This lowers the barrier to entry for smaller teams and startups utilizing the open-source nature of RISC-V.
• Customization and Extension Optimization: The flexibility of RISC-V, especially regarding custom extensions, requires highly tailored synthesis flows. ML offers the ability to learn the optimal synthesis steps specific to novel instruction set extensions, ensuring efficient hardware translation without extensive manual tuning.