Abstract

This paper presents a novel approach for electronic design automation (EDA) focusing on efficient timing closure during the physical design flow. It introduces a framework utilizing a Derivable Gradient Boosting Machine (D-GBM) model to accurately predict and optimize circuit timing. By integrating this derivable model directly into the placement stage, the method significantly reduces design iterations and improves overall efficiency compared to traditional predict-then-optimize flows.

Report

Efficient Timing Prediction and Optimization Using Derivable Gradient Boosting Machine Model at Placement Stage

Key Highlights

• Machine Learning Integration: The study focuses on integrating advanced Machine Learning (ML), specifically Gradient Boosting Machines (GBM), into the core of the physical design optimization process.
• Derivability for Optimization: The key innovation is making the timing prediction model derivable, enabling its direct use within optimization solvers (e.g., gradient descent) to adjust placement locations based on timing gradients.
• Early Stage Impact: The approach targets the placement stage—a critical, early step in physical design—to ensure timing constraints are met predictively before costly detailed routing begins.
• Efficiency Gain: The goal is to minimize the iterative cycle of placement, timing analysis, and refinement, leading to faster design convergence and reduced time-to-market.

Technical Details

• Model Architecture: Derivable Gradient Boosting Machine (D-GBM). Unlike standard black-box ML models, the structure is adapted to output differentiable functions, allowing continuous gradient calculation with respect to physical parameters (like cell coordinates).
• Application Focus: Timing prediction, specifically for critical path delay estimation and worst-case slack analysis.
• Optimization Strategy: The derivable nature allows the timing prediction function to serve as the objective or constraint function in the placement solver, enabling simultaneous prediction and optimization adjustment of cell positions.
• Context: The methodology addresses the persistent challenge in EDA where current placement tools struggle to accurately model timing impact (especially wire delay) early in the flow.

Implications

• Acceleration of Open Hardware (RISC-V): RISC-V development often benefits from highly efficient and fast EDA toolchains. By dramatically shortening the physical design cycle, this technology enables faster iteration for complex RISC-V cores (e.g., out-of-order pipelines or multi-core designs).
• Improved Performance: More accurate and predictive timing closure at the placement stage ensures that complex semiconductor designs can reliably hit higher target operating frequencies (Fmax).
• Competitiveness of EDA Tools: Integrating sophisticated, derivable ML models pushes the state-of-the-art in placement optimization, helping academic and potentially open-source EDA tools achieve performance comparable to, or exceeding, mature commercial offerings.
• Handling Design Complexity: As semiconductor technology scales and designs grow larger and denser, traditional heuristic optimization methods become less effective. ML-driven, gradient-based methods are crucial for managing the immense search space complexity inherent in modern chip layout.