Integrated neuromorphic photonics

Neuromorphic photonics is an emerging field at the intersection of photonics and neuromorphic engineering, with the goal of producing accelerated processors that combine the information processing capacity of neuromorphic processing architectures and the speed and bandwidth of photonics. It is motivated by the widening gap between current computing capabilities and computing needs that result from the limitations of conventional, microelectronic processors. Here, I will present these challenges, describe photonic neural-network approaches being developed by our lab and others, and offer a glimpse at this fields future.

[1]  Sae Woo Nam,et al.  Superconducting optoelectronic circuits for neuromorphic computing , 2016, ArXiv.

[2]  Paul R. Prucnal,et al.  Recent progress in semiconductor excitable lasers for photonic spike processing , 2016 .

[3]  Paul R. Prucnal,et al.  Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing , 2014, Journal of Lightwave Technology.

[4]  Bhavin J. Shastri,et al.  Neuromorphic Photonic Integrated Circuits , 2018, IEEE Journal of Selected Topics in Quantum Electronics.

[5]  Kaushik Roy,et al.  An All-Memristor Deep Spiking Neural Network: A Step Towards Realizing the Low Power, Stochastic Brain , 2017, ArXiv.

[6]  Bhavin J. Shastri,et al.  Neuromorphic photonic networks using silicon photonic weight banks , 2016, Scientific Reports.

[7]  Paul R. Prucnal,et al.  Spike processing with a graphene excitable laser , 2016, Scientific Reports.

[8]  P. Prucnal,et al.  NEUROMORPHIC PHOTONICS , 2017 .

[9]  A. Biberman,et al.  An ultralow power athermal silicon modulator , 2014, Nature Communications.

[10]  Ellen Zhou,et al.  Neuromorphic photonic networks using silicon photonic weight banks , 2017, Scientific Reports.

[11]  P. R. Prucnal,et al.  A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing , 2013, IEEE Journal of Selected Topics in Quantum Electronics.

[12]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[13]  M. C. Soriano,et al.  Advances in photonic reservoir computing , 2017 .