Deep neural networks (DNNs) have been emerged as the state-of-the-art algorithms in broad range of applications. To reduce the memory foot-print of DNNs, in particular for embedded applications, sparsification techniques have been proposed. Unfortunately, these techniques come with a large hardware overhead. In this paper, we present a hardware-aware pruning method where the locations of non-zero weights are derived in real-time from a Linear Feedback Shift Registers (LFSRs). Using the proposed method, we demonstrate a total saving of energy and area up to 63.96% and 64.23% for VGG-16 network on down-sampled ImageNet, respectively for iso-compression-rate and iso-accuracy.
@article{arxiv.1911.04468,
title = {Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random Indices},
author = {Foroozan Karimzadeh and Ningyuan Cao and Brian Crafton and Justin Romberg and Arijit Raychowdhury},
journal= {arXiv preprint arXiv:1911.04468},
year = {2019}
}