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.
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The authors thank G. Kenyon, P. Knag, T. Chen, Z. Zhang, Y. Jeong and M. Zidan for discussions and help. This work was support by the Defense Advanced Research Projects Agency (DARPA) through award no. HR0011-13-2-0015, by the Air Force Office of Scientific Research (AFOSR) through MURI grant FA9550-12-1-0038 and by the National Science Foundation (NSF) through grant CCF-1617315.
The authors declare no competing financial interests.
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Sheridan, P., Cai, F., Du, C. et al. Sparse coding with memristor networks. Nature Nanotech 12, 784–789 (2017). https://doi.org/10.1038/nnano.2017.83
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