Work

Empowering Vector Architectures for ML: The CAMP Architecture for Matrix Multiplication

Year: 2025

Type: conference-paper

Abstract: This study presents the Cartesian Accumulative Matrix Pipeline (CAMP) architecture, a novel approach designed to enhance matrix multiplication in Vector Architectures (VAs) and Single Instruction, Multiple Data (SIMD) units.CAMP improves processing efficiency of Quantized Neural Networks (QNNs).Matrix multiplication is a cornerstone of machine learning applications, and its quantized versions are increasingly popular for more efficient operations.Unfortunately, existing VAs and SIMD-support units struggle to efficiently handle these quantized formats.In this work, we propose CAMP, a simple yet effective architecture that leverages a hybrid multiplier.The CAMP architecture significantly advances the performance of vector architectures in handling quantized data, enabling more efficient execution of matrix multiplication across various platforms, specifically targeting the ARMv8 Scalable Vector Extension (SVE) and edge RISC-V with SIMD-support unit architecture.Thanks to its hierarchical design, CAMP natively supports multiple integer precisions; here we focus on 4-bit and 8-bit, while the unit remains applicable as a general-purpose integer

Source: Proceedings of the ... annual International Symposium on Microarchitecture/Proceedings of the annual International Symposium on Microarchitecture

DOI: 10.1145/3725843.3760547