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Titlebook: Neuro-inspired Computing Using Resistive Synaptic Devices; Shimeng Yu Book Jun 20171st edition Springer International Publishing AG 2017 n

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書目名稱Neuro-inspired Computing Using Resistive Synaptic Devices
編輯Shimeng Yu
視頻videohttp://file.papertrans.cn/664/663831/663831.mp4
概述Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-level.Includes detailed discussion of the periph
圖書封面Titlebook: Neuro-inspired Computing Using Resistive Synaptic Devices;  Shimeng Yu Book Jun 20171st edition Springer International Publishing AG 2017 n
描述.This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology..
出版日期Book Jun 20171st edition
關(guān)鍵詞neuro-inspired computing; resistive synaptic devices; Phase change memory; peripheral neuron circuits d
版次1
doihttps://doi.org/10.1007/978-3-319-54313-0
isbn_softcover978-3-319-85368-0
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Weight Tuning of Resistive Synaptic Devices and Convolution Kernel Operation on 12 × 12 Cross-Point 2D kernel matrix into 1D column vector, i.e., a column of the array, and enable the parallel readout of multiple 2D kernels simultaneously. As a proof-of-concept demonstration, we used the offline trained edge filters to detect both horizontal and vertical edges of the 20 × 20 pixels of black-and-wh
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Processing-In-Memory Architecture Design for Accelerating Neuro-Inspired Algorithmsoperations in neuro-inspired algorithms. Moreover, it provides a set of circuit and microarchitecture design as well as system-level support to implement various deep neural networks (DNNs). Benefiting from both the PIM architecture and the efficiency of using RRAM crossbar arrays for neuro-inspired
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Multilayer Perceptron Algorithm: Impact of Nonideal Conductance and Area-Efficient Peripheral Circuirections. In order to develop an intuitive understanding of the impact that various features of such jump tables have on the classification performance in the ANN application, we describe studies of various artificially constructed jump tables. We then assess the impact of undesired, time-varying co
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