Reducing Instruction-Fetch Energy in RISC-V for Embedded AI Processing via Dynamic and Static Loop Caching

Wiebren Wijnstra, Sameed Sohail, Berend-Jan van der Zwaag, Sabih Gerez, Amirreza Yousefzadeh

Embedded RISC-V processors are increasingly deployed for on-device AI inference at the edge, where energy efficiency is a primary design constraint. Instruction fetching from SRAM-based memory is a dominant source of energy consumption in these cores, accounting for over 40\% of total energy in our baseline measurements. This paper presents two loop cache architectures integrated into the datapath of a RISC-V processor: a dynamic loop cache that automatically detects and caches short backward-branch loops at runtime, and a static loop cache that functions as a software-managed hot-code instruction buffer, allowing preloading of arbitrary instruction blocks during the boot sequence. Both designs are implemented in the open-source NEORV32 RISC-V processor and evaluated on a LeNet-5 convolutional neural network inference workload, synthesized on GlobalFoundries 22nm FDX+ technology at 0.5\,V and 250\,MHz. The dynamic cache reduces instruction fetches by 48.3\% and total energy by 21.5\%, while the static cache achieves an 83.3\% fetch reduction and 35.5\% total energy savings. The area overhead of both designs remains below 0.2\% of the full SoC area. The complete implementation is open source.