Accurate Inference With Inaccurate RRAM Devices: A Joint Algorithm-Design Solution

Resistive random access memory (RRAM) is a promising technology for energy-efficient neuromorphic accelerators. However, when a pretrained deep neural network (DNN) model is programmed to an RRAM array for inference, the model suffers from accuracy degradation due to RRAM nonidealities, such as devi...

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Główni autorzy:	Gouranga Charan, Abinash Mohanty, Xiaocong Du, Gokul Krishnan, Rajiv V. Joshi, Yu Cao
Format:	Artigo
Język:	Inglês
Wydane:	IEEE 2020-01-01
Seria:	IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Hasła przedmiotowe:	Convolution neural networks device nonidealities model robustness neuromorphic computing random sparse adaptation (RSA) resistive random access memory (RRAM)
Dostęp online:	https://ieeexplore.ieee.org/document/9069242/
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