Crossbar-aligned & integer-only neural network compression for efficient in-memory acceleration

Crossbar-aligned & integer-only neural network compression for efficient in-memory acceleration

Crossbar-based In-Memory Computing (IMC) accelerators preload the entire Deep Neural Network (DNN) into crossbars before inference. However, devices with limited crossbars cannot infer increasingly complex models. IMC-pruning can reduce the usage of crossbars, but current methods need expensive extr...

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書目詳細資料
Main Authors:	Huai, Shuo, Liu, Di, Luo, Xiangzhong, Chen, Hui, Liu, Weichen, Subramaniam, Ravi
其他作者:	School of Computer Science and Engineering
格式:	Conference or Workshop Item
語言:	English
出版:	2023
主題:	Engineering::Computer science and engineering::Computing methodologies In-Memory Computing Pruning Quantization Neural Networks
在線閱讀:	https://hdl.handle.net/10356/165352
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

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