MX: Enhancing RISC-V's Vector ISA for Ultra-Low Overhead, Energy-Efficient Matrix Multiplication
Originally published on ArXiv - Hardware Architecture
Computer Science > Hardware Architecture
arXiv:2401.04012v1 (cs)
[Submitted on 8 Jan 2024]
Title:MX: Enhancing RISC-V's Vector ISA for Ultra-Low Overhead, Energy-Efficient Matrix Multiplication
Authors:Matteo Perotti, Yichao Zhang, Matheus Cavalcante, Enis Mustafa, Luca Benini
View a PDF of the paper titled MX: Enhancing RISC-V's Vector ISA for Ultra-Low Overhead, Energy-Efficient Matrix Multiplication, by Matteo Perotti and 4 other authors
Abstract:Dense Matrix Multiplication (MatMul) is arguably one of the most ubiquitous compute-intensive kernels, spanning linear algebra, DSP, graphics, and machine learning applications. Thus, MatMul optimization is crucial not only in high-performance processors but also in embedded low-power platforms. Several Instruction Set Architectures (ISAs) have recently included matrix extensions to improve MatMul performance and efficiency at the cost of added matrix register files and units. In this paper, we propose Matrix eXtension (MX), a lightweight approach that builds upon the open-source RISC-V Vector (RVV) ISA to boost MatMul energy efficiency. Instead of adding expensive dedicated hardware, MX uses the pre-existing vector register file and functional units to create a hybrid vector/matrix engine at a negligible area cost (< 3%), which comes from a compact near-FPU tile buffer for higher data reuse, and no clock frequency overhead. We implement MX on a compact and highly energy-optimized RVV processor and evaluate it in both a Dual- and 64-Core cluster in a 12-nm technology node. MX boosts the Dual-Core's energy efficiency by 10% for a double-precision 64x64x64 matrix multiplication with the same FPU utilization (~97%) and by 25% on the 64-Core cluster for the same benchmark on 32-bit data, with a 56% performance gain.
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Hardware Architecture (cs.AR)
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arXiv:2401.04012 [cs.AR]
(or arXiv:2401.04012v1 [cs.AR] for this version)
https://doi.org/10.48550/arXiv.2401.04012
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From: Matteo Perotti [view email]
[v1] Mon, 8 Jan 2024 16:44:21 UTC (209 KB)
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AI Analysis
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.
