MSE: A Matrix Sparsity Extension for RISC-V - Ribas, Luc; Aquino, Iago; Felzmann, Isaías; Wanner, Lucas; Araújo, Guido (Anais do Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD); 2025: Anais do XXVI Simpósio em Sistemas Computacionais de Alto DesempenhoDO - 10.5753/sscad.2025.16697, 2025)
The increase in size and computing demands of modern Convolutional Neural Networks (CNNs) requires techniques that can reduce model size and accelerate inference execution. A popular method is pruning, where some model weights are zeroed in a controlled way while maintaining model accuracy. This method can create structured sparsity patterns in the weight matrix, allowing storage in compressed formats. This reduces the model’s memory footprint while maintaining sequential access to the values. In this paper, we propose the Matrix Sparsity Extension (MSE). The proposed RISC-V instruction set extension takes advantage of the sparsity formats generated by pruning, reducing the amount of memory operations needed for matrix multiplication. MSE was able to provide speedups close to 1.24x over the baseline uncompressed operation, reaching 1.75x when the sparse case offers better alignment in cache.