Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used...

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Veröffentlicht in:	arXiv.org 2024-11
Hauptverfasser:	Yang, Junyi, Mao, Ruibin, Jiang, Mingrui, Cheng, Yichuan, Pao-Sheng, Vincent Sun, Dong, Shuai, Pedretti, Giacomo, Xia Sheng, Ignowski, Jim, Li, Haoliang, Li, Can, Basu, Arindam
Format:	Artikel
Sprache:	eng
Schlagworte:	Analog to digital conversion Arrays Artificial neural networks Computation Memory tasks Natural language processing Neural networks Recurrent neural networks Speech recognition
Online-Zugang:	Volltext
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