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

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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Bibliographic Details
Main Authors: Gouranga Charan, Abinash Mohanty, Xiaocong Du, Gokul Krishnan, Rajiv V. Joshi, Yu Cao
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Subjects:
Convolution neural networks
device nonidealities
model robustness
neuromorphic computing
random sparse adaptation (RSA)
resistive random access memory (RRAM)
Online Access:https://ieeexplore.ieee.org/document/9069242/

Internet

https://ieeexplore.ieee.org/document/9069242/

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