2021
DOI: 10.3390/photonics8090363
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Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits

Abstract: We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation. We find that tuning a pruned MRR weight banks model gives an equivalent performance in training with the model of random initialization. Results show that the overall accuracy of the optical neural network on the MNIST dataset is 93.49% after pruning six-layer MRR weight banks on the co… Show more

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Cited by 4 publications
(2 citation statements)
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“…Moreover, lower loss boosts the performance of passive structures and coherent light sources. These advantages have driven explosive growth in SiPh, opening up a plethora of new applications, from data centres 6 , to neural networks 11 , to Lidar 12 and to quantum photonics 13 .…”
Section: Mainmentioning
confidence: 99%
“…Moreover, lower loss boosts the performance of passive structures and coherent light sources. These advantages have driven explosive growth in SiPh, opening up a plethora of new applications, from data centres 6 , to neural networks 11 , to Lidar 12 and to quantum photonics 13 .…”
Section: Mainmentioning
confidence: 99%
“…Moreover, lower loss boosts the performance of passive structures and coherent light sources. Capitalizing on these advantages, along with reduced cost and large scale production, SiPh has seen explosive growth in the last few years, opening up a plethora of new applications from neural networks [4] to Lidar [5] to quantum photonics [6].…”
Section: Introductionmentioning
confidence: 99%