Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random
  Indices   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.

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The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th