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Computer Science > Machine Learning

arXiv:2301.13142 (cs)
[Submitted on 30 Jan 2023 (v1), last revised 31 Jan 2023 (this
version, v2)]

Title:Self-Compressing Neural Networks

Authors:Szabolcs Csefalvay, James Imber
View a PDF of the paper titled Self-Compressing Neural Networks, by
Szabolcs Cs\'efalvay and 1 other authors
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    Abstract:This work focuses on reducing neural network size, which
    is a major driver of neural network execution time, power
    consumption, bandwidth, and memory footprint. A key challenge is
    to reduce size in a manner that can be exploited readily for
    efficient training and inference without the need for specialized
    hardware. We propose Self-Compression: a simple, general method
    that simultaneously achieves two goals: (1) removing redundant
    weights, and (2) reducing the number of bits required to
    represent the remaining weights. This is achieved using a
    generalized loss function to minimize overall network size. In
    our experiments we demonstrate floating point accuracy with as
    few as 3% of the bits and 18% of the weights remaining in the
    network.

Comments: Accepted submission to 2023 DL-Hardware Co-Design for AI
          Acceleration
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2301.13142 [cs.LG]
          (or arXiv:2301.13142v2 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2301.13142
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Szabolcs Csefalvay [view email]
[v1] Mon, 30 Jan 2023 18:22:28 UTC (444 KB)
[v2] Tue, 31 Jan 2023 10:28:52 UTC (444 KB)
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