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

arXiv: https://arxiv.org/abs/1911.04468 · HTML: https://mlsanity.com/paper/1911.04468/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Towards In-Vehicle Multi-Task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Models](https://mlsanity.com/paper/2403.06088/) (2024)
- [Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation](https://mlsanity.com/paper/2406.18595/) (2024)

## Abstract (arXiv)

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.