Sparse coding with memristor networks

Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which bi...

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Bibliographic Details
Published in:Nature nanotechnology Vol. 12; no. 8; pp. 784 - 789
Main Authors: Sheridan, Patrick M., Cai, Fuxi, Du, Chao, Ma, Wen, Zhang, Zhengya, Lu, Wei D.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.08.2017
Nature Publishing Group
Subjects:
142/126
639/166/987
639/925/927/1007
Algorithms
Coding
Computer vision
Data processing
Dictionaries
Feature extraction
Image processing
Materials Science
Memristors
Nanotechnology
Nanotechnology and Microengineering
Nervous system
Neural coding
Neurosciences
Object recognition
Pattern matching
Pattern recognition
Power consumption
Signal processing
ISSN:1748-3387, 1748-3395, 1748-3395
Online Access:Get full text
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Summary:Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary. The implementation of bio-inspired sparse coding algorithms aimed at image processing is demonstrated by exploiting 32 × 32 crossbar arrays of analogue memristors.
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ISSN:1748-3387
1748-3395
1748-3395
DOI:10.1038/nnano.2017.83