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)
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.
Related research projects
POST-DIGITAL
POST-DIGITAL: Neuromorphic computing in photonic and other nonlinear media
I.P.: Ingo Fischer, Claudio Mirasso
POST-DIGITAL is a Marie Skłodowska-Curie Innovative Training Network, funded by the European Union’s Horizon 2020 research and innovation programme. POST-DIGITAL is committed to form a new generation of engineers and researchers, affording …
IDEA (CSIC)
Improving data DEcoding in optical communication networks All-optically using neuro-inspired photonic systems
I.P.: Ingo Fischer
In this project, it is our aim to develop novel all-optical decoding schemes for optical communication networks that are based on neuro-inspired concepts and are able to fulfill the previous requirements. Excellently …