Offset-Diagonal Computing for Large-scale, Compact, Efficient, and Sparsity-enabled Optical Processors

We introduce a fast, efficient, scalable, and sparsity-enabled optical computing system with minimal component requirements by combining spatial, temporal, and wavelength multiplexing with optical-delay-based operations. We experimentally demonstrate near-ideal performance on standard benchmarks.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.1364/cleo_at.2025.jss129_2
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

Offset-Diagonal Computing for Large-scale, Compact, Efficient, and Sparsity-enabled Optical Processors

Arjun Iyer, Yihang Zhu, William H. Renninger, Larry C. Young et al.
Neural Networks and Reservoir Computing
article

Offset-Diagonal Computing for Large-scale, Compact, Efficient, and Sparsity-enabled Optical Processors

Arjun Iyer, Yihang Zhu, William H. Renninger, Larry C. Young, Akhilesh Tyagi
article en

Abstract

We introduce a fast, efficient, scalable, and sparsity-enabled optical computing system with minimal component requirements by combining spatial, temporal, and wavelength multiplexing with optical-delay-based operations. We experimentally demonstrate near-ideal performance on standard benchmarks.

Openalex Percentile: Top 87%
Neural Networks and Reservoir Computing
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Offset-Diagonal Computing for Large-scale, Compact, Efficient, and Sparsity-enabled Optical Processors — Arjun Iyer, Yihang Zhu, et al. · (2025) | TGRS Research Map | TGRS