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A Survey of Neuromorphic Computing and Neural Networks in Hardware [article]

Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank
2017 arXiv   pre-print
In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history.  ...  approaches, hardware and devices, supporting systems, and finally applications.  ...  Image classification, detection, or recognition is an extremely popular application for neural networks and neuromorphic systems.  ... 
arXiv:1705.06963v1 fatcat:wsc7xpi3sjagri2qhc4herxxba

An Overview of Neuromorphic Computing for Artificial Intelligence Enabled Hardware-based Hopfield Neural Network

Zheqi Yu, Amir M. Abdulghani, Adnan Zahid, Hadi Heidari, Muhammad A. Imran, Qammer H. Abbasi.
2020 IEEE Access  
Many researchers have vastly invested in neuro-inspired models, algorithms, learning approaches, operation systems for the exploration of the neuromorphic system and have implemented many corresponding  ...  Towards the end, we conclude with a broad discussion and a viable plan for the latest application prospects to facilitate developers with a better understanding of the aforementioned model in accordance  ...  ACKNOWLEDGMENT Authors would like to thank Sultan Qaboos University (Government of the Sultanate of Oman) for supporting Dr. A. M. Abdulghani.  ... 
doi:10.1109/access.2020.2985839 fatcat:mclixaatyzbk7kn4lvshxw7aie

Event-based Vision: A Survey

Guillermo Gallego, Tobi Delbruck, Garrick Michael Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew Davison, Jorg Conradt, Kostas Daniilidis, Davide Scaramuzza
2020 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range.  ...  Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with  ...  The SpiNNaker has been coupled with event cameras for stereo depth estimation [149] , [232] , optic flow computation [232] , [233] , and for object tracking [234] and recognition [235] .  ... 
doi:10.1109/tpami.2020.3008413 pmid:32750812 fatcat:vlxvlv4uynh5rpw4qlmaywqlqq

Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

Xinyu Wu, Vishal Saxena, Kehan Zhu
2015 IEEE Journal on Emerging and Selected Topics in Circuits and Systems  
A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density  ...  As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive  ...  ACKNOWLEDGMENT The authors thank the anonymous reviewers for their help in improving this work with their comments on the manuscript writing and results presentation. The authors also thank Dr.  ... 
doi:10.1109/jetcas.2015.2433552 fatcat:jd6l2uhpmjhindanx7boqkdkgq

Neuromorphic Processing and Sensing: Evolutionary Progression of AI to Spiking [article]

Philippe Reiter, Geet Rose Jose, Spyridon Bizmpikis, Ionela-Ancuţa Cîrjilă
2020 arXiv   pre-print
A forward gaze into industrial, medical and commercial applications that can readily benefit from SNNs wraps up this investigation into the neuromorphic computing future.  ...  This paper explains the theoretical workings of neuromorphic technologies based on spikes, and overviews the state-of-art in hardware processors, software platforms and neuromorphic sensing devices.  ...  For explaining the biological analogies for SNNs, discussing the current research streams in the neuromorphic domain, and inspiring the Nengo CNN-to-SNN conversion experiment detailed in this paper, we  ... 
arXiv:2007.05606v1 fatcat:mw7nczubnzao3l73kyibxyvjpy

Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

Indar Sugiarto, Felix Pasila, R.H. Setyobudi, E. Alasaarela, F. Pasila, G. Chan, S.-G. Lee
2018 MATEC Web of Conferences  
In this paper, a neuromorphic platform called SpiNNaker is described and evaluated in order to understand its potential use as a platform for a deep learning approach.  ...  To achieve this, neuromorphic systems are developed to mimic the brain functionality down to cellular level.  ...  study and the discussion with the SpiNNaker team as well as with the experts in the field of neuromorphic technology.  ... 
doi:10.1051/matecconf/201816401015 fatcat:yenldx2mrrdzhhpcipuntbhl5y

Parallel Computing for Brain Simulation

L. A. Pastur-Romay, A. B. Porto-Pazos, F. Cedron, A. Pazos
2017 Current Topics in Medicinal Chemistry  
Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before  ...  It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques).  ...  Hasler and Marr [113] showed an analysis of analog neuromorphic hardware systems and compared them with the digital and biological systems. Indiveri et al.  ... 
doi:10.2174/1568026617666161104105725 pmid:27823566 fatcat:wlcngyt5ubcrxpyhzyepjlsqyu

Advances in Machine Learning and Deep Neural Networks

Rama Chellappa, Sergios Theodoridis, Andre van Schaik
2021 Proceedings of the IEEE  
Machine learning is a promising enabler for the fifth-generation (5G) communication systems and beyond.  ...  At the center of this historical happening, as one of the key enabling technologies, lies a discipline that deals with data and whose goal is to extract information and related knowledge that is hidden  ...  Edition, 2009), the coauthor of the book Introduction to Pattern Recognition: A MATLAB Approach (Academic Press, 2010), and the coeditor of the book Efficient Algorithms for Signal Processing and System  ... 
doi:10.1109/jproc.2021.3072172 fatcat:j3xryj6jerh45g7zmu37sfuhtu

Event-based Vision: A Survey [article]

Guillermo Gallego, Tobi Delbruck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew Davison, Joerg Conradt, Kostas Daniilidis, Davide Scaramuzza
2020 arXiv   pre-print
Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as high speed and high dynamic range.  ...  Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with  ...  The SpiNNaker has been coupled with event cameras for stereo depth estimation [161] , [257] , optic flow computation [257] , [258] , and for object tracking [259] and recognition [260] .  ... 
arXiv:1904.08405v2 fatcat:ffh6el7ojfg6jag5qm2hwnhweu

Biologically Inspired Intensity and Depth Image Edge Extraction

Dermot Kerr, Sonya Coleman, Thomas Martin McGinnity
2018 IEEE Transactions on Neural Networks and Learning Systems  
In this paper, we present a depth and intensity image feature extraction approach that has been inspired by biological vision systems.  ...  However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real-time.  ...  In [21] a hierarchical SNN is used for a visual attention system and in [30] for a categorisation system.  ... 
doi:10.1109/tnnls.2018.2797994 pmid:29994457 fatcat:66mdav5fafarnowuo6g7e6qgv4

Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving [article]

Wei Wang, Shibo Zhou, Jingxi Li, Xiaohua Li, Junsong Yuan, Zhanpeng Jin
2020 arXiv   pre-print
Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving.  ...  To the best of our knowledge, this is the first attempt to use SNN to directly perform object recognition on raw temporal pulses.  ...  Besides accuracy, the SNN object recognition system was also evaluated for the recognition time and data points required for recognition (ratio to total), as indicated by T rec and R data .  ... 
arXiv:2001.09220v1 fatcat:judb6j2runduvh7yu4hgo4avnu

Neurorobots as a Means Toward Neuroethology and Explainable AI

Kexin Chen, Tiffany Hwu, Hirak J. Kashyap, Jeffrey L. Krichmar, Kenneth Stewart, Jinwei Xing, Xinyun Zou
2020 Frontiers in Neurorobotics  
We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.  ...  In this paper, we review neurorobot experiments by focusing on how the robot's behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ... 
doi:10.3389/fnbot.2020.570308 pmid:33192435 pmcid:PMC7604467 fatcat:37v42xxymbfuznceh4gofmuawy

Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision

Yongduan Song, Jennie Si, Sonya Coleman, Dermot Kerr
2022 IEEE Transactions on Neural Networks and Learning Systems  
propose a novel spiking neural network using three biologically plausible modules to imitate how multiple brain regions work together to create visual guidance when manipulating fragile objects in a tight  ...  propose a novel edge-computing system for image recognition via memristor-based blaze block circuit.  ... 
doi:10.1109/tnnls.2022.3161003 fatcat:4e6v2kclcbb5pgkqqsyyaiwzjy

Unsupervised learning of digit recognition using spike-timing-dependent plasticity

Peter U. Diehl, Matthew Cook
2015 Frontiers in Computational Neuroscience  
We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity  ...  We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity  ...  Acknowledgments We would like to thank Damien Querlioz, Oliver Bichler, and the reviewers for helpful comments. This work was supported by the SNF Grant 200021-143337 "Adaptive Relational Networks."  ... 
doi:10.3389/fncom.2015.00099 pmid:26941637 pmcid:PMC4522567 fatcat:wepvikz37bavhhlxzk3aol2qgu

MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation [article]

Tao Liu, Zihao Liu, Fuhong Lin, Yier Jin, Gang Quan, Wujie Wen
2018 arXiv   pre-print
Time-based spiking neural network has recently emerged as a promising solution in Neuromorphic Computing System designs for achieving remarkable computing and power efficiency within a single chip.  ...  However, the relevant research activities have been narrowly concentrated on the biological plausibility and theoretical learning approaches, causing inefficient neural processing and impracticable multilayer  ...  System Architecture As a realization of multilayer fully-connected spiking neural network (SNN), MT-Spike is inspired from biological spiking neuron models and able to work in "training" and "testing"  ... 
arXiv:1803.05117v1 fatcat:6jlqjso3gfc6pno5scr53uzxeu
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