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Neuromorphic scaling advantages for energy-efficient random walk computation [article]

J. Darby Smith, Aaron J. Hill, Leah E. Reeder, Brian C. Franke, Richard B. Lehoucq, Ojas Parekh, William Severa, James B. Aimone
2021 arXiv   pre-print
Such random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks.  ...  Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities.  ...  Acknowledgments We thank Steve Plimpton and Andrew Baczewski for reviewing an early version of the manuscript and Adam Moody, Suma Cardwell, and Craig Vineyard for managing access to the TrueNorth and  ... 
arXiv:2107.13057v1 fatcat:6saeb5a4jrhqto5scokhg4x4zq

Spiking Neural Algorithms for Markov Process Random Walk [article]

William Severa, Rich Lehoucq, Ojas Parekh, James B. Aimone
2018 arXiv   pre-print
We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware.  ...  The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications.  ...  ., for the U.S. Department of Energy's National Nuclear Security Administration under contract de-na0003525.  ... 
arXiv:1805.00509v1 fatcat:ciwqtixhdvflvi6mcpzqjcznae

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype

Yexin Yan, David Kappel, Felix Neumaerker, Johannes Partzsch, Bernhard Vogginger, Sebastian Hoeppner, Steve Furber, Wolfgang Maass, Robert Legenstein, Christian Mayr
2019 IEEE Transactions on Biomedical Circuits and Systems  
This, combined with fitting the model into to the local static random access memory (SRAM), leads to 62% energy reduction compared to the case without accelerators and the use of external dynamic random  ...  By making efficient use of the hardware accelerators and numerical optimizations, the computation time of one plasticity update is reduced by a factor of 2.  ...  In addition, this work was supported by the Center for Advancing Electronics Dresden (cfaed) and the H2020-  ... 
doi:10.1109/tbcas.2019.2906401 pmid:30932847 fatcat:wagyzgfit5cqjfojmd44g6hmue

Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate [article]

Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber, Benjamin Cramer, Andreas Baumbach, Dominik Dold, Julian Göltz, Akos F. Kungl, Timo C. Wunderlich, Andreas Hartel, Eric Müller, Oliver Breitwieser (+12 others)
2019 arXiv   pre-print
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment  ...  The high acceleration factor of 1000 compared to biological dynamics enables the execution of computationally expensive tasks, by allowing the fast emulation of long-duration experiments or rapid iteration  ...  Each experiment started with a spread-out phase, in which a virtual insect performed a random walk starting from a certain origin.  ... 
arXiv:1912.12980v1 fatcat:74gzvnkyorehdey3lt3yzogvw4

Bio-inspired Gait Imitation of Hexapod Robot Using Event-Based Vision Sensor and Spiking Neural Network [article]

Justin Ting, Yan Fang, Ashwin Sanjay Lele, Arijit Raychowdhury
2020 arXiv   pre-print
We propose a bio-inspired feed-forward approach based on neuromorphic computing and event-based vision to address the gait imitation problem.  ...  Learning how to walk is a sophisticated neurological task for most animals. In order to walk, the brain must synthesize multiple cortices, neural circuits, and diverse sensory inputs.  ...  Recently, SNNs have been implemented on neuromorphic computing hardware to increase energy efficiency [10] , [11] .  ... 
arXiv:2004.05450v1 fatcat:h2vq4abx4rgazhi6fpb6hdla6u

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

Mike Davies, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, Sumedh R. Risbud
2021 Proceedings of the IEEE  
Such spiking neural networks (SNNs) naturally provide energy efficiency by preferring inactive states and low-latency processing by operating in an asynchronous, event-driven manner.  ...  that is natively suited for classes of brain-inspired computation that challenge the von Neumann model.  ...  To truly unlock the value of neuromorphic computing at scale, offering compelling power and latency advantages for all manner of computing devices processing real-world data streams, the sensors themselves  ... 
doi:10.1109/jproc.2021.3067593 fatcat:krqdmy3u6jdvfl7btjglek5ag4

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

Marc Osswald, Sio-Hoi Ieng, Ryad Benosman, Giacomo Indiveri
2017 Scientific Reports  
We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings.  ...  While machine vision has spawned a variety of software algorithms to solve the stereocorrespondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains  ...  for provision of the ROLLS chip.  ... 
doi:10.1038/srep40703 pmid:28079187 pmcid:PMC5227683 fatcat:6aisuvwmj5hu5amizfzktkckuu

Percolation with plasticity for neuromorphic computing [article]

V. G. Karpov, Maria Patmiou
2019 arXiv   pre-print
We introduce the percolation with plasticity (PWP) systems that exhibit neuromorphic functionalities including multi-valued memory, random number generation, matrix-vector multiplication, and associative  ...  PWP networks offer some advantages over the existing neural network architectures.  ...  We describe each bond as a random walk.  ... 
arXiv:1910.10535v1 fatcat:uvjkxp32lzebza27nkdlqi4m6i

An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel's Loihi [article]

Ioannis Polykretis, Konstantinos P. Michmizos
2020 arXiv   pre-print
that would result in inherently energy-efficient systems.  ...  Our results pave the way for scaling this and other approaches towards Loihi-controlled locomotion in autonomous mobile robots.  ...  This work aims to translate the advantages, including that of simplicity, carried by traditional oscillator-based CPG models [5, 40] into the emerging neuromorphic chips that promise significant energy-efficiency  ... 
arXiv:2006.04765v1 fatcat:qla2ywmuwjb5dduyxmatdiyvna

Adaptive Extreme Edge Computing for Wearable Devices

Erika Covi, Elisa Donati, Xiangpeng Liang, David Kappel, Hadi Heidari, Melika Payvand, Wei Wang
2021 Frontiers in Neuroscience  
We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors.  ...  We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.  ...  Stefan Slesazeck for useful discussion on ferroelectric and memristive devices.  ... 
doi:10.3389/fnins.2021.611300 pmid:34045939 pmcid:PMC8144334 fatcat:5by77im5crcslgt7zj3wulzd5e

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

Yan Fang, Zheng Wang, Jorge Gomez, Suman Datta, Asif I. Khan, Arijit Raychowdhury
2019 Frontiers in Neuroscience  
We show that the designed neuromorphic system can serve as an optimization solver with high-performance and high energy-efficiency.  ...  In the proposed computing paradigm, we use SNNs to represent agents in the swarm and encode problem solutions with the spike firing rate and with spike timing.  ...  ., 2018), are facilitating real-time large-scale mixed-signal neuromorphic computing systems with the potential to bridge the energy efficiency gap between engineered systems and biological systems.  ... 
doi:10.3389/fnins.2019.00855 pmid:31456659 pmcid:PMC6700359 fatcat:uxt7mczfvbdmtcqrv2dagm7dqi

Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities [article]

Jean-Matthieu Maro, Ryad Benosman
2019 arXiv   pre-print
We also introduce a new publicly available event-based dataset for gesture recognition selected through a clinical process to allow human-machine interactions for the visually-impaired and the elderly.  ...  This paper introduces a framework of gesture recognition operating on the output of an event based camera using the computational resources of a mobile phone.  ...  We also would like to thanks Antonio Fernandez, Andrew Watkinson and Gregor Lenz for their contribution in the android application.  ... 
arXiv:1811.07802v2 fatcat:iubmem3hjjb7rjdunhdax2onbe

Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications

Bruno Romeira, José M. L. Figueiredo, Julien Javaloyes
2017 Chaos  
With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and  ...  The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing.  ...  This approach takes advantage of energy efficient optical interconnects to achieve low-power neuron-like responses at speeds one billion times faster than neurons (>1 Gb/s).  ... 
doi:10.1063/1.5008888 pmid:29195310 fatcat:jhcdbj3xonhthhwz5grdexuheq

Closed-Loop Neuromorphic Benchmarks

Terrence C. Stewart, Travis DeWolf, Ashley Kleinhans, Chris Eliasmith
2015 Frontiers in Neuroscience  
Minimal simulation has been shown to lead to robust real-world performance, while still maintaining the practical advantages of simulation, such as making it easy for the same benchmark to be used by many  ...  Evaluating the effectiveness and performance of neuromorphic hardware is difficult.  ...  ACKNOWLEDGMENTS We thank Andrew Mundy for extensive work on the Nengo SpiNNaker backend, and James Knight for his prototype SpiNNaker implementation of the learning rule used here.  ... 
doi:10.3389/fnins.2015.00464 pmid:26696820 pmcid:PMC4678234 fatcat:5fiqiuwkqfaong7x72cosuaxji

Eurolab-4-HPC Long-Term Vision on High-Performance Computing [article]

Theo Ungerer, Paul Carpenter
2018 arXiv   pre-print
Radical changes in computing are foreseen for the next decade.  ...  The objective of the Eurolab-4-HPC vision is to provide a long-term roadmap from 2023 to 2030 for High-Performance Computing (HPC).  ...  The quantum random walk finds the exit node exponentially faster than a classical random walk. • Boolean Formula Algorithm can determine a winner in a two player game by performing a quantum random walk  ... 
arXiv:1807.04521v1 fatcat:5neetrgubjhnvcajcktpkohrzq
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