Introduction to JSTQE Issue on Photonics for Deep Learning and Neural Computing

Abstract

The papers in this special section examine neuromorphic photonics which combines optical physics and unconventional computing, resulting in a new class of ultrafast information processors for neuromorphic information and signal processing, machine learning, and high-performance computing. These processors can enable applications where low latency, high bandwidth, and low switching energies are paramount. Fundamentally, such computing concepts heavily depend on interconnects, a functionality where photonic processors can significantly outperform electronic systems. By combining the high bandwidth and efficiency of photonic devices with the adaptive, parallelism and complexity similar to the brain, photonic neural networks have the potential to be faster than conventional neural networks, while consuming less energy.