Sophisticated deep learning with on-chip optical diffractive tensor processing

Ever-growing deep-learning technologies are making revolutionary changes for modern life.However,conventional computing architectures are designed to process sequential and digital programs but are burdened with performing massive parallel and adaptive deep-learning applications.Photonic integrated circuits provide an efficient approach to mitigate bandwidth limitations and the power-wall brought on by its electronic counterparts,showing great potential in ultrafast and energy-free high-performance computation.Here,we propose an optical computing architecture enabled by on-chip diffraction to implement convolutional acceleration,termed“optical convolution unit”(OCU).We demonstrate that any real-valued convolution kernels can be exploited by the OCU with a prominent computational throughput boosting via the concept of structral reparameterization.With the OCU as the fundamental unit,we build an optical convolutional neural network(oCNN)to implement two popular deep learning tasks:classification and regression.For classification,Fashion Modified National Institute of Standards and Technology(Fashion-MNIST)and Canadian Institute for Advanced Research(CIFAR-4)data sets are tested with accuracies of 91.63%and 86.25%,respectively.For regression,we build an optical denoising convolutional neural network to handle Gaussian noise in gray-scale images with noise levelσ=10,15,and 20,resulting in clean images with an average peak signal-to-noise ratio(PSNR)of 31.70,29.39,and 27.72 dB,respectively.The proposed OCU presents remarkable performance of low energy consumption and high information density due to its fully passive nature and compact footprint,providing a parallel while lightweight solution for future compute-in-memory architecture to handle high dimensional tensors in deep learning.

Integrated diffractive optical neural network with space-time interleaving

Integrated diffractive optical neural networks(DONNs)have significant potential for complex machine learning tasks with high speed and ultralow energy consumption.However,the on-chip implementation of a high-performance optical neural network is limited by input dimensions.In contrast to existing photonic neural networks,a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed,high-dimensional,and all-optical input signal modulation.To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation,an on-chip DONN with a designed footprint of 0.0945 mm~2is proposed to resolve the vowel recognition task,reaching a computation speed of about 1.4×10^(13)operations per second and yielding an accuracy of 98.3%in numerical calculation.In addition,the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally.This method can realize the on-chip DONN with higher input dimension and lower energy consumption.

Multimode diffractive optical neural network

On-chip diffractive optical neural networks(DONNs)bring the advantages of parallel processing and low energy consumption.However,an accurate representation of the optical field’s evolution in the structure cannot be provided using the previous diffraction-based analysis method.Moreover,the loss caused by the open boundaries poses challenges to applications.A multimode DONN architecture based on a more precise eigenmode analysis method is proposed.We have constructed a universal library of input,output,and metaline structures utilizing this method,and realized a multimode DONN composed of the structures from the library.On the designed multimode DONNs with only one layer of the metaline,the classification task of an Iris plants dataset is verified with an accuracy of 90%on the blind test dataset,and the performance of the one-bit binary adder task is also validated.Compared to the previous architectures,the multimode DONN exhibits a more compact design and higher energy efficiency.