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A Hardware-Friendly Approach Towards Sparse Neural Networks Based on LFSR-Generated Pseudo-Random Sequences by Karimzadeh, Foroozan; Cao, Ningyuan; Crafton, Brian; Romberg, Justin; Raychowdhury, Arijit is a Engineering article available to read on EtoBox.

What is A Hardware-Friendly Approach Towards Sparse Neural Networks Based on LFSR-Generated Pseudo-Random Sequences about?

The increase in the number of edge devices has led to the emergence of edge computing where the computations are performed on the device. In recent years, deep neural networks (DNNs) have become the state-of-the-art method in a broad range of applications, from image recognition, to cognitive tasks to control. However, neural network models are typically large and computationally expensive and therefore not deployable on power and memory constrained edge devices. Sparsification techniques have been proposed to reduce the memory foot-print of neural network models. However, they typically lead to substantial hardware and memory overhead. In this article, we propose a hardware-aware pruning method using linear feedback shift register (LFSRs) to generate the locations of non-zero weights in real-time during inference. We call this LFSR-generated pseudorandom sequence based sparsity (LGPS) technique. We explore two different architectures for our hardware-friendly LGPS technique, based on (1) row/column indexing with LFSRs and (2) column-wise indexing with nested LFSRs, respectively. Using the proposed method, we present a total saving of energy and area up to 37.47% and 49.93% respect

Who reads A Hardware-Friendly Approach Towards Sparse Neural Networks Based on LFSR-Generated Pseudo-Random Sequences?

Author

Karimzadeh, Foroozan; Cao, Ningyuan; Crafton, Brian; Romberg, Justin; Raychowdhury, Arijit

Publisher

Institute of Electrical and Electronics Engineers; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 1549-8328)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)