Paper Info
Title
Joint Source-Channel Coding with Neural Networks for Analog Data Compression and Storage
Abstract
We provide an encoding and decoding strategy for efficient storage of analog data onto an array of Phase-Change Memory (PCM) devices. The PCM array is treated as an analog channel, with the stochastic relationship between write voltage and read resistance for each device determining its theoretical capacity. The encoder and decoder are implemented as neural networks with parameters that are trained end-to-end to minimize distortion for a fixed number of devices. To minimize distortion, the encoder and decoder must adapt jointly to the statistics of images and the statistics of the channel. Similar to Balle et al. (2017), we find that incorporating divisive normalization in the encoder, paired with de-normalization in the decoder, improves model performance. We show that the autoencoder achieves a rate-distortion performance above that achieved by a separate JPEG source coding and binary channel coding scheme. These results demonstrate the feasibility of exploiting the full analog dynamic range of PCM or other emerging memory devices for efficient storage of analog image data.
Year
DOI
Venue
2018
10.1109/DCC.2018.00023
2018 Data Compression Conference
Keywords
Field
DocType
Joint Source Channel Coding,Neural Networks,Natural Images
Computer vision,Autoencoder,Computer science,Communication channel,JPEG,Artificial intelligence,Encoder,Decoding methods,Analog signal,Computer hardware,Distortion,Encoding (memory)
Conference
ISSN
ISBN
Citations 
1068-0314
978-1-5386-4884-1
2
PageRank 
References 
Authors
0.39
9
6
Name
Order
Citations
PageRank
Ryan Zarcone120.39
Dylan M. Paiton221.40
Alex Anderson320.72
Jesse H. Engel432620.21
H.-S. Philip Wong5645106.40
Bruno A. Olshausen649366.79