In this review paper, we discuss the properties and applications of photonic computing and analog signal processing. Photonic computational circuits have large operation bandwidth, low power consumption, and fine frequency control, enabling a wide range of application-specific computational techniques that are impossible to implement using traditional electrical and digital hardware alone. These advantages are illustrated in the elegant implementation of optical steganography, the real-time blind separation of signals in the same bandwidth, and the efficient acceleration of artificial neural network inference. The working principles and use of photonic circuits for analog signal processing and neuromorphic computing are reviewed and notable demonstrated applications are highlighted.

Artificial synapses utilizing spike signals are essential elements of new generation brain-inspired computers. In this paper, we realize light-stimulated adaptive artificial synapse based on nanocrystalline zinc oxide film. The artificial synapse photoconductivity shows spike-type signal response, long and short-term memory (LTM and STM), STM-to-LTM transition and paired-pulse facilitation. It is also retaining the memory of previous exposures and demonstrates spike-frequency adaptation properties. A way to implement neurons with synaptic depression, tonic excitation, and delayed accelerating types of response under the influence of repetitive light signals is discussed. The developed artificial synapse is able to become a key element of neuromorphic chips and neuromorphic sensorics systems.

The explosive volume growth of deep-learning (DL) applications has triggered an era in computing, with neuromorphic photonic platforms promising to merge ultra-high speed and energy efficiency credentials with the brain-inspired computing primitives. The transfer of deep neural networks (DNNs) onto silicon photonic (SiPho) architectures requires, however, an analog computing engine that can perform tiled matrix multiplication (TMM) at line rate to support DL applications with a large number of trainable parameters, similar to the approach followed by state-of-the-art electronic graphics processing units. Herein, we demonstrate an analog SiPho computing engine that relies on a coherent architecture and can perform optical TMM at the record-high speed of 50 GHz. Its potential to support DL applications, where the number of trainable parameters exceeds the available hardware dimensions, is highlighted through a photonic DNN that can reliably detect distributed denial-of-service attacks within a data center with a Cohen’s kappa score-based accuracy of 0.636.