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Hand-written letters classification measurement data

Citation

Ashtiani, Farshid; Geers, Alexander; Aflatouni, Firooz (2022), Hand-written letters classification measurement data, Dryad, Dataset, https://doi.org/10.5061/dryad.q2bvq83mw

Abstract

Deep neural networks with applications from computer vision to medical diagnosis1-5 are commonly implemented using clock-based processors6-14, where computation speed is mainly limited by the clock frequency and the memory access time. In the optical domain, despite advances in photonic computation15-17, the lack of scalable on-chip optical nonlinearity and the loss of photonic devices limit the scalability of optical deep networks. Here we report the first integrated end-to-end photonic deep neural network (PDNN) that performs sub-nanosecond image classification through direct processing of the optical waves impinging on the on-chip pixel array as they propagate through layers of neurons. Within each neuron, linear computation is performed optically and the nonlinear activation function is realised opto-electronically, enabling a classification time of under 570 ps, which is comparable with a single clock-cycle of state-of-the-art digital platforms. A uniformly distributed supply light provides the same per-neuron optical output range enabling scalability to large-scale PDNNs. Two- and four-class classification of handwritten letters with accuracies of higher than 93.8% and 89.8% are demonstrated, respectively. Direct clock-less processing of optical data eliminates analogue-to-digital conversion and the requirement for a large memory module, enabling faster and more energy-efficient neural networks for the next generations of deep learning systems.

Funding

Office of Naval Research, Award: N00014-19-1-2248