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arXiv · 2503.06183

Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers

Francesco Daghero·Daniele Jahier Pagliari·Francesco Conti·Luca Benini·Massimo Poncino·Alessio Burrello
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Abstract

The acceleration of pruned Deep Neural Networks (DNNs) on edge devices such as Microcontrollers (MCUs) is a challenging task, given the tight area- and power-constraints of these devices. In this work, we propose a three-fold contribution to address this problem. First, we design a set of optimized software kernels for N:M pruned layers, targeting ultra-low-power, multicore RISC-V MCUs, which are up to 2.1x and 3.4x faster than their dense counterparts at 1:8 and 1:16 sparsity, respectively. Then, we implement a lightweight Instruction-Set Architecture (ISA) extension to accelerate the indirect load and non-zero indices decompression operations required by our kernels, obtaining up to 1.9x extra speedup, at the cost of a 5% area overhead. Lastly, we extend an open-source DNN compiler to utilize our sparse kernels for complete networks, showing speedups of 3.21x and 1.81x on a ResNet18 and a Vision Transformer (ViT), with less than 1.5% accuracy drop compared to a dense baseline.

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Francesco Daghero, Daniele Jahier Pagliari, Francesco Conti, Luca Benini, Massimo Poncino, Alessio Burrello. 2025-03-19. Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers. https://arxiv.org/abs/2503.06183

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