RISC-V Architecture for Neural Network Acceleration

Abstract

With the growing computational demands of neural networks, modern processors must provide efficient acceleration while maintaining flexibility. RISC-V, an open-source instruction set architecture (ISA), has emerged as a promising alternative for neural network acceleration due to its extensibility, customization, and hardware-software co-design capabilities. This paper explores how RISC-V architecture benefits neural networks, focusing on its scalability, vector processing capabilities, and dedicated extensions such as the RISC-V Vector (RVV) and Bit Manipulation (Bitmanip) extensions. We analyze its performance in deep learning workloads, compare it with conventional architectures, and propose optimizations for enhanced efficiency.