Optimizing Structured-Sparse Matrix Multiplication in RISC-V Vector Processors

Abstract

This work analyzes and optimizes Structured-Sparse Matrix Multiplication (SSMM) performance for Machine Learning applications running on RISC-V Vector Processors. The authors propose and integrate a new instruction, vindexmac (vector index-multiply-accumulate), designed to facilitate indirect reads and reduce executed instructions per matrix multiplication iteration. Integrating this single, low-cost instruction into a decoupled vector processor achieves substantial runtime efficiency, demonstrating performance improvements of 25% and 33% compared to existing, highly-optimized vectorized kernels.

Report

Key Highlights

• Focus Area: Optimization of Structured-Sparse Matrix Multiplication (SSMM) critical for Machine Learning (ML) acceleration.
• Hardware Target: RISC-V Vector Processors.
• Key Innovation: Proposal and integration of a new custom instruction, vindexmac (vector index-multiply-accumulate).
• Performance Gains: The custom instruction improves runtime efficiency by 25% and 33% when compared against highly-optimized vectorized kernels using only the existing RISC-V ISA.
• Implementation Cost: The proposed instruction was integrated with negligible hardware cost.

Technical Details

• Initial Analysis: Comprehensive exploration of SSMM implementation using the current RISC-V instruction set vector extension, focusing on performance critical parameters.
• Optimization Parameters Analyzed: Impact of data distribution across scalar and vector register files, data locality, and the effectiveness of loop unrolling.
• New Instruction (vindexmac): A single instruction designed to enable indirect reads from the vector register file.
• Mechanism: vindexmac reduces the total number of instructions executed per matrix multiplication iteration and avoids introducing additional dependencies that would restrict loop unrolling.
• Testbed Architecture: The optimization and the new instruction were validated using a decoupled RISC-V vector processor architecture.
• Application Scope: Experimental results specifically demonstrated efficiency gains when executing state-of-the-art Convolutional Neural Networks (CNNs).

Implications

• RISC-V ISA Enhancement: This work demonstrates the practical and significant performance benefits gained by strategically extending the RISC-V vector instruction set (V-extension) for specialized ML workloads.
• ML Acceleration: The optimization addresses a core challenge in hardware acceleration—efficiently handling structured sparse data, making RISC-V more competitive for high-performance ML inference and training.
• Hardware Efficiency: Achieving up to 33% runtime improvement with only a single, low-cost instruction validates the potential for targeted, modular hardware extensions within the RISC-V ecosystem to unlock significant computational power.