CNN and RVV Co-design for Efficient Model Serving

Paper in proceeding, 2025

Convolutional algorithm performance depends on layer dimensions, with SIMD demands and cache sharing influencing runtime selection. To identify the best settings, we perform a co-design exploration of convolutional layer parameters and three algorithms: Direct, im2col+GEMM, and Winograd, jointly with hardware parameters for RISC-V vector architectures. Our results show that incorporating hardware parameters with layer dimensions boosts execution time and efficiency, emphasizing the need for co-design.