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Register a new userAll-optical spiking neurosynaptic networks with self-learning capabilities
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Software-implementation, via neural networks, of brain-inspired computing approaches underlie many important modern-day computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, differing from real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy brain-like computing difficult to achieve. To overcome such limitations, an attractive and alternative goal is to design direct hardware mimics of brain neurons and synapses which, when connected in appropriate networks (or neuromorphic systems), process information in a way more fundamentally analogous to that of real brains. Here we present an all-optical approach to achieving such a goal. Specifically, we demonstrate an all-optical spiking neuron device and connect it, via an integrated photonics network, to photonic synapses to deliver a small-scale all-optical neurosynaptic system capable of supervised and unsupervised learning. Moreover, we exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain using a photonic system comprising 140 elements. Such optical implementations of neurosynaptic networks promise access to the high speed and bandwidth inherent to optical systems, which would be very attractive for the direct processing of telecommunication and visual data in the optical domain.
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All-optical binary convolution with a photonic spiking vertical-cavity surface-emitting laser (VCSEL) neuron is proposed and demonstrated experimentally for the first time. Optical inputs, extracted from digital images and temporally encoded using rectangular pulses, are injected in the VCSEL neuron which delivers the convolution result in the number of fast (<100 ps long) spikes fired. Experimental and numerical results show that binary convolution is achieved successfully with a single spiking VCSEL neuron and that all-optical binary convolution can be used to calculate image gradient magnitudes to detect edge features and separate vertical and horizontal components in source images. We also show that this all-optical spiking binary convolution system is robust to noise and can operate with high-resolution images. Additionally, the proposed system offers important advantages such as ultrafast speed, high energy efficiency and simple hardware implementation, highlighting the potentials of spiking photonic VCSEL neurons for high-speed neuromorphic image processing systems and future photonic spiking convolutional neural networks.
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