Prospects and applications of photonic neural networks

Abstract

Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks.

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Cited by in corpus (5)

• The physics of optical computing
• Scaling Up Silicon Photonic-based Accelerators: Challenges and Opportunities
• Integrated Photonic Tensor Processing Unit for a Matrix Multiply: a Review
• Heavy tails and pruning in programmable photonic circuits
• Slimmed optical neural networks with multiplexed neuron sets and a corresponding backpropagation training algorithm