MX: Enhancing RISC-V's Vector ISA for Ultra-Low Overhead, Energy-Efficient Matrix Multiplication

Abstract

The paper proposes Matrix eXtension (MX), a lightweight enhancement to the open-source RISC-V Vector (RVV) ISA designed to significantly boost the energy efficiency of Dense Matrix Multiplication (MatMul). MX avoids expensive dedicated matrix units by leveraging pre-existing vector hardware alongside a compact near-FPU tile buffer, incurring a negligible area cost of less than 3% with no frequency overhead. Implemented in a 12-nm technology node, MX delivered a 25% energy efficiency improvement and a 56% performance gain on 32-bit MatMul within a 64-Core cluster.

Report

Key Highlights

• Lightweight Enhancement: MX (Matrix eXtension) is proposed as a minimal-overhead method to enhance the RISC-V Vector (RVV) ISA specifically for Matrix Multiplication (MatMul).
• Hardware Efficiency: The design utilizes a hybrid vector/matrix engine approach by reusing the existing RVV vector register file and functional units, avoiding the costly addition of dedicated matrix registers and units.
• Low Cost: The extension results in a negligible area cost (less than 3%) and zero clock frequency overhead.
• Performance Gains: MX yielded substantial improvements, including a 10% energy efficiency boost for double-precision 64x64x64 MatMul on a Dual-Core system, and a 25% energy efficiency gain combined with a 56% performance gain on a 64-Core cluster using 32-bit data.

Technical Details

• Architecture: MX builds upon the standard RVV ISA, effectively turning the vector engine into a hybrid vector/matrix engine.
• Data Reuse Mechanism: The primary hardware addition required for MX is a compact near-FPU tile buffer, which is implemented specifically to increase data reuse during MatMul operations.
• Implementation Technology: The evaluation was performed on a compact and highly energy-optimized RVV processor cluster fabricated using a 12-nm technology node.
• Utilization: During evaluation, the FPU utilization rate remained extremely high, measured at approximately 97%.
• Benchmarks: Testing included dense matrix multiplications of size 64x64x64 using both double-precision and 32-bit floating-point data types.

Implications

• Cost-Effective Acceleration: MX provides a critical, ultra-low overhead solution for implementing high-performance MatMul capabilities, which are essential for machine learning (ML), linear algebra, and digital signal processing (DSP).
• RISC-V Competitiveness: By enhancing RVV efficiency significantly without requiring major dedicated hardware investment, MX strengthens RISC-V's position in the embedded and low-power computing markets against architectures that rely on expensive proprietary matrix extensions.
• Scalability for Embedded AI: The demonstrated energy and performance improvements in both Dual-Core and 64-Core clusters show that MX is highly suitable for embedded low-power platforms requiring scalable AI inference capabilities.