DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-Chip Training

DNN+NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks, with hierarchical design options from device-level, to circuit level and up to algorithm level. A python wrapper is developed to interface NeuroSim with a popular machine learning plat...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems Jg. 40; H. 11; S. 2306 - 2319
Hauptverfasser:	Peng, Xiaochen, Huang, Shanshi, Jiang, Hongwu, Lu, Anni, Yu, Shimeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerators Algorithms Analog to digital conversion Analog to digital converters Artificial neural networks Benchmark testing Circuit design Common Information Model (computing) Computer architecture Computer memory Deep learning emerging nonvolatile memory (eNVM) Hardware hardware accelerator in-memory computing Inference Integrated circuit modeling Machine learning Microprocessors on-chip training System-on-chip Training Weight
ISSN:	0278-0070, 1937-4151
Online-Zugang:	Volltext
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