Error-resilient analog image storage and compression with analog-valued RRAM arrays: an adaptive joint source-channel coding approach
Published in IEEE International Electron Devices Meeting (IEDM), 2018
Abstract:
We demonstrate by experiment an image storage and compression task by directly storing analog image data onto an analog-valued RRAM array. A joint source-channel coding algorithm is developed with a neural network to encode and retrieve natural images. The encoder and decoder adapt jointly to the statistics of the images and the statistics of the RRAM array in order to minimize distortion. This adaptive joint source-channel coding method is resilient to RRAM array non-idealities such as cycle-to-cycle and device-to-device variations, time-dependent variability, and non-functional storage cells, while achieving a reasonable reconstruction performance of ~ 20 dB using only 0.1 devices/pixel for the analog image.
Recommended citation:
Xin Zheng, Ryan Zarcone, Dylan M. Paiton, Joon Sohn, Weier Wan, Bruno Olshausen, and H-S. Philip Wong. “Error-resilient analog image storage and compression with analog-valued RRAM arrays: an adaptive joint source-channel coding approach.” In 2018 IEEE International Electron Devices Meeting (IEDM), pp. 3-5. IEEE, 2018.
@INPROCEEDINGS{zheng2018error,
author={Zheng, Xin and Zarcone, Ryan and Paiton, Dylan and Sohn, Joon and Wan, Weier and Olshausen, Bruno and Wong, H. -S. Philip},
booktitle={2018 IEEE International Electron Devices Meeting (IEDM)},
title={Error-Resilient Analog Image Storage and Compression with Analog-Valued RRAM Arrays: An Adaptive Joint Source-Channel Coding Approach},
year={2018},
volume={},
number={},
pages={3.5.1-3.5.4},
doi={10.1109/IEDM.2018.8614612}
}