Silicon photonics for energy-efficient neuromorphic computing

Bassem Tossoun

doi:10.1117/12.2656106

11 January 2023 Silicon photonics for energy-efficient neuromorphic computing

Bassem Tossoun

Proceedings Volume 12334, Emerging Applications in Silicon Photonics III; 1233403 (2023) https://doi.org/10.1117/12.2656106
Event: SPIE Photonex, 2022, Birmingham, United Kingdom

Abstract

New machine learning algorithms such as deep neural networks and the availability of large datasets have created a large drive towards new types of hardware capable of executing these algorithms with higher energy-efficiency. Recently, silicon photonics has emerged as a promising hardware platform for neuromorphic computing due to its inherent capability to process linear and non-linear operations and transmit a high bandwidth of data in parallel. At Hewlett Packard Labs, an energy-efficient dense-wavelength division multiplexing (DWDM) silicon photonics platform has been developed as the underlying foundation for innovative neuromorphic computing architectures. The latest research on our silicon photonic neuromorphic platform will be presented and discussed.

Citation Download Citation

Bassem Tossoun "Silicon photonics for energy-efficient neuromorphic computing", Proc. SPIE 12334, Emerging Applications in Silicon Photonics III, 1233403 (11 January 2023); https://doi.org/10.1117/12.2656106

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