2024/11/23 by Amir Ofir, Ofir, Amir, Gil Ben-Artzi +1
Computer Science · #Cellular Automata and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Interconnection Networks and Systems #Quantum-Dot Cellular Automata
paper · pdf · doi:10.48550/arxiv.2411.15659
openalex publication_date 2024/11/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a novel approach for accelerating convolutions during inference for CPU-based architectures. The most common method of computation involves packing the image into the columns of a matrix (im2col) and performing general matrix multiplication (GEMM) with a matrix of weights. This results in two main drawbacks: (a) im2col requires a large memory buffer and can experience inefficient memory access, and (b) while GEMM is highly optimized for scientific matrices multiplications, it is not well suited for convolutions. We propose an approach that takes advantage of scalar-matrix multiplication and reduces memory overhead. Our experiments with commonly used network architectures demonstrate a significant speedup compared to existing indirect methods.