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 condition of low insertion loss. This work is scalable to much more complex networks, such as convolutional neural networks and recurrent neural networks, and provides a potential guide for truly large-scale optical neural networks.
Highlights
In the past twenty years, deep neural networks have become a milestone in artificial intelligence for its superior performance in various fields, such as autonomous driving [1], stock forecasting [2], intelligent translation [3], and image recognition [4]
It is obvious that the error curve in the training process is relatively smooth, while the regularization coefficient is not, which means the process of weight differentiation does not lead to sharp fluctuations in training and has little effect on pruning results
We demonstrate a three-ply optical neural network based on a pruned
Summary
In the past twenty years, deep neural networks have become a milestone in artificial intelligence for its superior performance in various fields, such as autonomous driving [1], stock forecasting [2], intelligent translation [3], and image recognition [4]. For the reason of enormous computation in matrix multiplication, traditional central processing units are gradually becoming suboptimal for implementing deep learning algorithms. Silicon photonics [5] provide superior performance in energy consumption [6,7] and computational rate [8] over electronics, which has become an attractive platform. Photonic integrated circuits can realize matrix multiplication with the coherence and superposition of linear optics [9]. The programmable Mach Zehnder interferometers realize optical interference units for optical neural networks [11,16], and MRRs are used for optical matrixvector multipliers to implement neuromorphic photonic networks [17,18,19]. Diffractive optics [20,21,22], space-light modulators [23,24], semiconductor optical amplifiers [25], optical modulators [26,27], and other related optical devices are used to achieve powerful deep learning accelerators, which can perform machine learning tasks with ultra-low energy consumption [28]
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