Backpropagation through nonlinear units for all-optical training of neural networks
Photonics Research Optical Society of America
Abstract:Backpropagation through nonlinear neurons is an outstanding challenge to the field of optical neural networks and the major conceptual barrier to all-optical training schemes. Each neuron is required to exhibit a directionally dependent response to propagating optical signals, with the backwards response conditioned on the forward signal, which is highly non-trivial to implement optically. We propose a practical and surprisingly simple solution that uses saturable absorption to provide the network nonlinearity. We find that the backward propagating gradients required to train the network can be approximated in a pump-probe scheme that requires only passive optical elements. Simulations show that, with readily obtainable optical depths, our approach can achieve equivalent performance to state-of-the-art computational networks on image classification benchmarks, even in deep networks with multiple sequential gradient approximations. This scheme is compatible with leading optical neural network proposals and therefore provides a feasible path towards end-to-end optical training.
Fully reconfigurable coherent optical vector–matrix multiplication
Optics Letters Optical Society of America 45:20 (2020) 5752-5755
Abstract:Optics is a promising platform in which to help realize the next generation of fast, parallel, and energy-efficient computation. We demonstrate a reconfigurable free-space optical multiplier that is capable of over 3000 computations in parallel, using spatial light modulators with a pixel resolution of only 340×340. This enables vector–matrix multiplication and parallel vector–vector multiplication with vector size of up to 56. Our design is, to the best of our knowledge, the first to simultaneously support optical implementation of reconfigurable, large-sized, and real-valued linear algebraic operations. Such an optical multiplier can serve as a building block of special-purpose optical processors such as optical neural networks and optical Ising machines.
Single photon at a configurable quantum-memory-based beam splitter
Physical Review A American Physical Society (APS) 97:6 (2018) 63805
Mirrorless Optical Parametric Oscillation with Tunable Threshold in Cold Atoms
Physical Review Letters American Physical Society (APS) 119:15 (2017) 150406
Quantum Heat Engine Using Electromagnetically Induced Transparency
Physical Review Letters American Physical Society (APS) 119:5 (2017) 50602