JEFF SHERWIN TIZON

Comparative Analysis of RISC-V Custom Vector Extensions for Edge Neural Network Inference

Executing deep learning models on sub-watt edge devices is severely constrained by memory bandwidth limits and control-path overheads. This research presents a custom hardware-software co-design using an open-source 32-bit RISC-V architecture optimized with specialized packed low-precision (INT8/INT4) vector extensions...