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Joint Source-Channel Coding with Neural Networks for Analog Data Compression and Storage
- Source :
- DCC
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
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.
- Subjects :
- 010302 applied physics
business.industry
Computer science
Data_CODINGANDINFORMATIONTHEORY
02 engineering and technology
computer.file_format
01 natural sciences
Autoencoder
JPEG
Analog signal
Distortion
Encoding (memory)
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
business
Encoder
computer
Decoding methods
Computer hardware
Computer Science::Information Theory
Communication channel
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 Data Compression Conference
- Accession number :
- edsair.doi...........b1e270137768a947e8285376dd698a9a