# Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random Indices

Source: [Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random Indices](<https://www.sota2.com/research/paper/68fa66ae1e209131cea11af3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:56:21.324Z

Authors: Foroozan Karimzadeh, Ningyuan Cao, Brian Crafton, Justin Romberg, Arijit Raychowdhury

Published: 2019-11-09T17:26:54.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.04468>)

[PDF](<https://arxiv.org/pdf/1911.04468.pdf>)