Abstract

This paper presents microarchitectural optimizations for energy-efficient RISC-V clusters aimed at achieving near zero-stall matrix multiplication for machine learning workloads. Key innovations include "zero-overhead loop nests" and a novel "zero-conflict memory subsystem" leveraging a double-buffering-aware interconnect. These enhancements result in near-ideal utilization (up to 99.4%), yielding 11% performance and 8% energy efficiency improvements over state-of-the-art baselines while retaining full programmability.

Report

Structured Report: Towards Zero-Stall Matrix Multiplication

Key Highlights

• Performance Goal: Achieving near-zero stall performance during matrix multiplication (matmul) on energy-efficient RISC-V clusters.
• Utilization: The optimized microarchitecture achieved near-ideal processor utilizations ranging between 96.1% and 99.4%.
• Performance Uplift: The solution demonstrated an 11% performance improvement compared to the baseline State-of-the-Art (SoA) RISC-V cluster.
• Energy Efficiency: The enhancements resulted in an 8% increase in energy efficiency over the baseline cluster.
• Flexibility: The approach retains a fully-programmable, general-purpose solution, allowing support for a significantly wider range of workloads compared to dedicated, specialized accelerators.

Technical Details

• Target Architecture: Clusters of lightweight RISC-V instruction processors designed for flexibility and efficiency in ML acceleration.
• Loop Overhead Elimination: The paper introduces "zero-overhead loop nests" to remove control overheads associated with loop handling.
• Memory Conflict Resolution: A "zero-conflict memory subsystem" was implemented to eliminate bank conflicts in the shared multi-banked L1 memory.
• Interconnect Novelty: The zero-conflict system utilizes a novel double-buffering-aware interconnect specifically designed to manage memory access patterns efficiently and prevent conflicts.
• Comparison: The resulting system achieved comparable utilization and performance to a specialized SoA accelerator, demonstrating only a 12% difference in energy efficiency despite being a general-purpose solution.

Implications

• RISC-V Competitiveness: This work significantly bolsters RISC-V's position in the high-performance ML acceleration market by demonstrating that programmable processor clusters can achieve utilization rates previously reserved for highly specialized, fixed-function hardware.
• Flexible ML Deployment: By addressing fundamental bottlenecks (control overheads and memory stalls) without sacrificing programmability, the design enables highly efficient ML operations on adaptable platforms, crucial for edge AI requiring rapid model updates.
• Architectural Blueprint: The introduction of specific techniques like "zero-overhead loop nests" and the "double-buffering-aware interconnect" provides a concrete, low-overhead microarchitectural blueprint for future RISC-V cluster designs focused on compute-intensive tasks.