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      EqSpike: spike-driven equilibrium propagation for neuromorphic implementations


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          Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks. In this work, we develop a spiking neural network algorithm called EqSpike, compatible with neuromorphic systems, which learns by equilibrium propagation. Through simulations, we obtain a test recognition accuracy of 97.6% on the MNIST handwritten digits dataset (Mixed National Institute of Standards and Technology), similar to rate-based equilibrium propagation, and comparing favorably to alternative learning techniques for spiking neural networks. We show that EqSpike implemented in silicon neuromorphic technology could reduce the energy consumption of inference and training, respectively, by three orders and two orders of magnitude compared to graphics processing units. Finally, we also show that during learning, EqSpike weight updates exhibit a form of spike-timing-dependent plasticity, highlighting a possible connection with biology.

          Graphical abstract


          • EqSpike is a spiking neural network version of equilibrium propagation

          • It achieves 97.6% test accuracy on MNIST with a fully connected architecture

          • Its two-factor local learning rule is compatible with neuromorphic hardware

          • Its weight updates exhibit a form of spike-timing-dependent plasticity


          Computer Science; Algorithms; Artificial Intelligence

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          Nanoscale memristor device as synapse in neuromorphic systems.

          A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal-oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.
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            Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.

            Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts. Copyright © 2014, American Association for the Advancement of Science.
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              Loihi: A Neuromorphic Manycore Processor with On-Chip Learning


                Author and article information

                20 February 2021
                19 March 2021
                20 February 2021
                : 24
                : 3
                [1 ]Thales Research and Technology, 91767 Palaiseau, France
                [2 ]Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767 Palaiseau, France
                [3 ]Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, 91120 Palaiseau, France
                Author notes
                []Corresponding author julie.grollier@ 123456cnrs-thales.fr
                S2589-0042(21)00190-5 102222
                © 2021 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).


                computer science,algorithms,artificial intelligence
                computer science, algorithms, artificial intelligence


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