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In this work we investigate the integration of optical and electronic hardware to create ML and DL accelerators with reduced computational complexity and energy consumption. We compare the performance of optical or hybrid optical-electronic neural networks and verify that by using these computation architectures it is possible to perform classification tasks with performances comparable with standard electronic neural networks but saving computational resources up to a factor of 10.
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Mengxiang Chen, Steffen Schoenhardt, Min Gu, Elena Goi, "Hybrid diffractive neural networks for energy efficient image classification," Proc. SPIE 12768, Holography, Diffractive Optics, and Applications XIII, 127682E (29 November 2023); https://doi.org/10.1117/12.3008372