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1. Primer on silicon neuromorphic photonic processors: architecture and compiler

   https://doi.org/10.1515/nanoph-2020-0172  

   Ferreira de Lima, Thomas; Tait, Alexander N.; Mehrabian, Armin; Nahmias, Mitchell A.; Huang, Chaoran; Peng, Hsuan-Tung; Marquez, Bicky A.; Miscuglio, Mario; El-Ghazawi, Tarek; Sorger, Volker J.; et al (August 2020, Nanophotonics)

   Abstract Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing. 

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2. Neuromorphic Photonic Networks

   Shastri, Bhavin J; Bilodeau, Simon; Marquez, Bicky A; Tait, Alexander N; de Lima, Thomas F; Huang, Chaoran; Chrostowski, Lukas; Shekhar, Sudip; Prucnal, Paul R (July 2021, Optical Fiber Communication Conference (OFC))

   Dong, P.; Kani, J.; Xie, C.; Casellas, R.; Cole, C.; Li, M. (Ed.)

   Neuromorphic photonics exploit optical device physics for neuron models, and optical interconnects for distributed, parallel, and analog processing for high-bandwidth, low-latency and low switching energy applications in artificial intelligence and neuromorphic computing. 

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3. Silicon Photonics for Artificial Intelligence and Neuromorphic Computing

   https://doi.org/10.1109/SUM48717.2021.9505837  

   Shastri, Bhavin J.; de Lima, Thomas Ferreira; Huang, Chaoran; Marquez, Bicky A.; Shekhar, Sudip; Chrostowski, Lukas; Prucnal, Paul R. (July 2021, in 2021 IEEE Photonics Society Summer Topicals Meeting Series (SUM))

   null (Ed.)

   Artificial intelligence and neuromorphic computing driven by neural networks has enabled many applications. Software implementations of neural networks on electronic platforms are limited in speed and energy efficiency. Neuromorphic photonics aims to build processors in which optical hardware mimic neural networks in the brain. 

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4. Neuromorphic Photonics: Current Status and Challenges

   https://doi.org/10.1109/ECOC48923.2020.9333362  

   Prucnal, Paul R.; de Lima, Thomas Ferrieira; Huang, Chaoran; Marquez, Bicky A.; Shastri, Bhavin J. (December 2020, 2020 European Conference on Optical Communications (ECOC))

   null (Ed.)

   Artificial intelligence and neuromorphic computing driven by neural networks has enabled many applications. Software implementations of neural networks on electronic platforms are limited in speed and energy efficiency. Neuromorphic photonics aims to build processors in which optical hardware mimic neural networks in the brain. 

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5. Progress in neuromorphic photonics

   https://doi.org/10.1515/nanoph-2016-0139  

   Ferreira de Lima, Thomas; Shastri, Bhavin J.; Tait, Alexander N.; Nahmias, Mitchell A.; Prucnal, Paul R. (January 2017, Nanophotonics)

   Abstract As society’s appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications. 

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