Diagonal structured sparsity for efficient optical and GPU-based neural networks

Diagonally-structured sparse neural networks suitable for optical implementation are shown to outperform other standard sparsity structures across several models and datasets, and are experimentally shown to have faster inference times than unstructured sparsity on GPUs.

Authors

Yuhao Zhu (ORCID: https://orcid.org/0000-0002-2802-0578)
Arjun Iyer (ORCID: https://orcid.org/0000-0002-2007-5742)
Abhishek Tyagi (ORCID: https://orcid.org/0000-0001-5538-8805)
Christopher Kanan (ORCID: https://orcid.org/0000-0002-6412-995X)
William H. Renninger

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1364/cleo_at.2025.jss129_4
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Diagonal structured sparsity for efficient optical and GPU-based neural networks

Yuhao Zhu, Arjun Iyer, Abhishek Tyagi, Christopher Kanan et al.

Jan 1, 2025

Neural Networks and Reservoir Computing

article

Diagonal structured sparsity for efficient optical and GPU-based neural networks

Yuhao Zhu, Arjun Iyer, Abhishek Tyagi, Christopher Kanan, William H. Renninger

January 1, 2025article en

Abstract

Diagonally-structured sparse neural networks suitable for optical implementation are shown to outperform other standard sparsity structures across several models and datasets, and are experimentally shown to have faster inference times than unstructured sparsity on GPUs.

Openalex Percentile: Top 87%

Neural Networks and Reservoir Computing

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Diagonal structured sparsity for efficient optical and GPU-based neural networks — Yuhao Zhu, Arjun Iyer, et al. · (2025) | TGRS Research Map | TGRS