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1. 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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2. 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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3. Powering next-generation industry 4.0 by a self-learning and low-power neuromorphic system

   https://doi.org/doi.org/10.1145/3411295.3411302  

   Hongyu An, Dong Sam (September 2020, ACM International Conference on Nanoscale Computing and Communication)

   null (Ed.)

   With the continuous development of technologies, our society is approaching the next stage of industrialization. The Fourth Industrial Revolution also referred to as Industry 4.0, redefines the manufacturing system as a smart and connected machinery system with fully autonomous operation capability. Several advanced cutting-edge technologies, such as cyber-physical systems (CPS), the internet of things (IoT), and artificial intelligence, are believed to the essential components to realize Industry 4.0. In this paper, we focus on a comprehensive review of how artificial intelligence benefits Industry 4.0, including potential challenges and possible solutions. A panoramic introduction of neuromorphic computing is provided, which is one of the most promising and attractive research directions in artificial intelligence. Subsequently, we introduce the vista of the neuromorphic-powered Industry 4.0 system and survey a few research activities on applications of artificial neural networks for IoT. 

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4. From memristive devices to neuromorphic systems

   https://doi.org/10.1063/5.0133044  

   Huang, Yi; Kiani, Fatemeh; Ye, Fan; Xia, Qiangfei (March 2023, Applied Physics Letters)

   Progress in hardware and algorithms for artificial intelligence (AI) has ushered in large machine learning models and various applications impacting our everyday lives. However, today's AI, mainly artificial neural networks, still cannot compete with human brains because of two major issues: the high energy consumption of the hardware running AI models and the lack of ability to generalize knowledge and self-adapt to changes. Neuromorphic systems built upon emerging devices, for instance, memristors, provide a promising path to address these issues. Although innovative memristor devices and circuit designs have been proposed for neuromorphic computing and applied to different proof-of-concept applications, there is still a long way to go to build large-scale low-power memristor-based neuromorphic systems that can bridge the gap between AI and biological brains. This Perspective summarizes the progress and challenges from memristor devices to neuromorphic systems and proposes possible directions for neuromorphic system implementation based on memristive devices. 

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5. Neuromorphic applications in medicine

   https://doi.org/10.1088/1741-2552/aceca3  

   Aboumerhi, Khaled; Güemes, Amparo; Liu, Hongtao; Tenore, Francesco; Etienne-Cummings, Ralph (August 2023, Journal of Neural Engineering)

   Abstract In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility. 

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