Communication-Avoiding Fusion of GEMM-Based Convolutions for Deep Learning in the RISC-V GAP8 MCU

Incorporating deep learning (DL) technologies to the edge is crucial for improving the security, privacy, and energy efficiency of the Internet of Things (IoT). In this scenario, the limitations of edge devices in terms of power dissipation, memory capacity, and processing power require a careful selection and optimization of algorithms for IoT DL applications. In this line, our work focuses on the convolution operator, a key component in deep neural networks for signal processing and computer vision. Specifically, the work aims at the efficient implementation of the lowering-based implementation of this operator, on the GAP8 parallel ultra-low power platform (PULP), with the goal of mitigating the data transfer costs across the memory hierarchy. Our contributions include 1) an analytical model for estimating the parallel execution time, 2) the exploration of different configuration options, and 3) four variants of the algorithm that fuse several components to address memory bottlenecks in the method. Overall, our best-fused variant provides a speedup of up to 1.25× over the baseline algorithm when applied to infer MobileNet-v1+ImageNet and VGG9+CIFAR10 using 8 threads, and up to 1.34× for ResNet18+ImageNet.