Introduction to JSTQE Issue on Photonics for Deep Learning and Neural Computing
Prucnal, P.R.; Shastri, B.J.; Fischer, I.; Brunner, D.
IEEE Journal of Selected Topics in Quantum Electronics 26 (1), 0200103 (1-3) (2020)
NEUROMORPHIC (i.e., neuron-isomorphic) photonics 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 func- tionality where photonic processors can significantly outperform electronic systems. By combining the high bandwidth and ef- ficiency 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.