Energy-efficient Non-Neural Face Recognition on the Multi-core RISC-V System

Feature extraction at remotely located edge nodes poses a challenge due to resource scarcity in terms of computation power, memory footprint, and battery life. Compared to deep neural network (DNN) algorithms for feature extraction, this work targets non-neural face recognition algorithms with comparable accuracy at a reduced memory footprint for small data sets. Mixed precision (8-bit fixed-point and 32-bit floating-point) training data is considered in this work for higher accuracy. Additionally, energy consumption is reduced by deploying a Memory Oriented MAnual tIling (MOMAI)-based non-neural face recognition (FR) algorithm on the GAP8 cluster. We have customized data transfer flow with MOMAI (similar to GAP8’s NNtool with Auto-tiler) and parallelized mixed precision training data of a non-neural Eigenfaces-based FR algorithm. Compared to non-DMA non-quantized manually tiled versions, DMA-based full manual-tiling strategy reduced recognition time by 16.09× on 8-cores and 134.32× on 1-core, respectively. Furthermore, an 8-bit quantization reduces the model size by 14.95× and overall cycle counts by 55.75%.