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...

詳細記述

書誌詳細

主要な著者:	Gouranga Charan, Abinash Mohanty, Xiaocong Du, Gokul Krishnan, Rajiv V. Joshi, Yu Cao
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2020-01-01
シリーズ:	IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
主題:	Convolution neural networks device nonidealities model robustness neuromorphic computing random sparse adaptation (RSA) resistive random access memory (RRAM)
オンライン･アクセス:	https://ieeexplore.ieee.org/document/9069242/
タグ:	タグ追加 タグなし, このレコードへの初めてのタグを付けませんか!