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Demonstrates energy reduction by 6.6x in photonic neural networks, indicating scalable weight sharing.
Silicon photonic neural networks often rely on thermal tuning of weights, limiting scaling for deep learning tasks. We demonstrate a time-domain weight sharing method that can reduce reconfigurations and energy by 6.6x without compromising accuracy.
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Zhang et al. (2025) studied this question.
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