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Title: Low-power computing with neuromorphic engineering
Authors: Liu, DB
Yu, H
Chai, Y 
Issue Date: Feb-2021
Source: Advanced intelligent systems, Feb. 2021, v. 3, no. 2, 2000150
Abstract: The increasing power consumption in the existing computation architecture presents grand challenges for the performance and reliability of very-large-scale integrated circuits. Inspired by the characteristics of the human brain for processing complicated tasks with low power, neuromorphic computing is intensively investigated for decreasing power consumption and enriching computation functions. Hardware implementation of neuromorphic computing with emerging devices substantially reduces power consumption down to a few mWcm(-2), compared with the central processing unit based on conventional Si complementary metal-oxide semiconductor (CMOS) technologies (50-100Wcm(-2)). Herein, a brief introduction on the characteristics of neuromorphic computing is provided. Then, emerging devices for low-power neuromorphic computing are overviewed, e.g., resistive random access memory with low power consumption (<pJ) per synaptic event. A few computation models for artificial neural networks (NNs), including spiking neural network (SNN) and deep neural network (DNN), which boost power efficiency by simplifying the computing procedure and minimizing memory access are discussed. A few examples for system-level demonstration are described, such as mixed synchronous-asynchronous and reconfigurable convolution neuron network (CNN)-recurrent NN (RNN) for low-power computing.
Keywords: In-memory computing
Low power neuromorphic computing
Nonvolatile memories
Synaptic devices
Publisher: Wiley-VCH Verlag GmbH & Co. KGaA
Journal: Advanced intelligent systems 
EISSN: 2640-4567
DOI: 10.1002/aisy.202000150
Rights: © 2020 The Authors. Published by Wiley-VCH GmbH.
This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The following publication Liu, D., Yu, H., & Chai, Y. (2021). Low‐Power Computing with Neuromorphic Engineering. Advanced Intelligent Systems, 3(2), 2000150 is available at https://doi.org/10.1002/aisy.202000150
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