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Co-designing Photonic Accelerators for Machine Learning on the Edge

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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

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Abstract

Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy-efficiency and inference performance compared to CPUs and GPUs. In this chapter, we propose a novel co-designed neural network accelerator called CrossLight that leverages silicon photonics. CrossLight includes device-level engineering for resilience to process variations and thermal crosstalk, circuit-level tuning enhancements for inference latency reduction, and architecture-level optimization to enable higher resolution, better energy-efficiency, and improved throughput. On average, CrossLight offers 9.5× lower energy per bit and 15.9× higher performance per watt at 16-bit resolution than state-of-the-art photonic deep learning accelerators.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, 1373 Campus Delivery, Colorado State University, Fort Collins, CO, USA

    Febin P. Sunny, Asif Mirza, Mahdi Nikdast & Sudeep Pasricha

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  1. Febin P. Sunny
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  2. Asif Mirza
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  3. Mahdi Nikdast
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  4. Sudeep Pasricha
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Correspondence to Febin P. Sunny .

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Editors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

  2. New York University Abu Dhabi, Abu Dhabi, Abu Dhabi, United Arab Emirates

    Muhammad Shafique

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Sunny, F.P., Mirza, A., Nikdast, M., Pasricha, S. (2024). Co-designing Photonic Accelerators for Machine Learning on the Edge. In: Pasricha, S., Shafique, M. (eds) Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-39932-9_10

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  • DOI: https://doi.org/10.1007/978-3-031-39932-9_10

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