Hardware-Aware Neural Network Compilation with Learned Optimization: A RISC-V Accelerator Approach

Abstract

The XgenSilicon ML Compiler is an automated end-to-end framework that optimizes high-level machine learning models into highly efficient RISC-V assembly code for custom ASIC accelerators. This system unifies software and hardware cost modeling, employing a learned multi-algorithm auto-tuning approach, dynamic shape support, and advanced quantization techniques. The resultant ASICs demonstrate significant Power, Performance, and Area (PPA) improvements, achieving 2.5-4.5x better performance and 3-6x lower power consumption compared to baseline implementations.

Report

Key Highlights

• Automated End-to-End Compilation: The XgenSilicon ML Compiler autonomously transforms high-level models into hardware-validated, ASIC-ready RISC-V assembly code.
• Significant PPA Gains: Evaluation shows accelerators compiled using this method achieve 2.5-4.5x performance increases, 3-6x reduction in power consumption, and 40-60 percent area savings.
• Learned Optimization Core: Utilizes a multi-algorithm auto-tuning framework with five distinct search strategies (Bayesian Optimization, Genetic Algorithm, Simulated Annealing, Random Search, and Grid Search) guided by a learned cost model.
• Hardware Validation Guarantee: The framework includes hardware-aware validation ensuring 100 percent ISA compliance and memory constraint satisfaction.
• Broad Operator Support: Supports over 100 ONNX operators, including advanced RISC-V Vector optimizations.

Technical Details

• Compiler Name: XgenSilicon ML Compiler.
• Optimization Strategy: Unification of software and hardware cost models across the compilation stack.
• Optimization Algorithms: Five integrated search strategies (Bayesian Optimization, Genetic Algorithm, Simulated Annealing, Random Search, Grid Search) coupled with a learned cost model for PPA tuning.
• Quantization: Integrated framework supporting extreme precisions (FP32 down to Binary), utilizing full KL divergence calibration (2048-bin histogram optimization) and momentum-based Quantization-Aware Training (QAT) gradient updates.
• Modeling Capabilities: Includes dynamic shape support with multi-configuration specialization and advanced cache-aware cost modeling incorporating multi-level cache hierarchy analysis.
• Output: Generates hardware-validated RISC-V assembly code suitable for direct ASIC synthesis.

Implications

• Accelerating RISC-V ASIC Design: This compiler significantly lowers the barrier for developing specialized, high-efficiency RISC-V accelerators for Machine Learning, eliminating the need for extensive manual optimization efforts.
• Establishing PPA Leadership: The reported performance and power efficiency metrics (up to 6x power reduction) position RISC-V as an extremely competitive platform for power-constrained ML deployments at the edge.
• Improving Ecosystem Robustness: By automating complex steps like ISA compliance checking and memory management, the framework increases the reliability and predictability of the RISC-V hardware development process.
• Advancing ML Compiler Technology: The integration of five optimization search strategies with a learned, unified cost model represents a major advancement in hardware-aware neural network compilation methodology, pushing beyond traditional single-algorithm solutions.