Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 27, Issue 11, Pages 2327-2336Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2482220
Keywords
Analog computing; crossbar; feature extraction; memristor; resistive switching; sparse coding
Categories
Funding
- Defense Advanced Research Projects Agency [HR0011-13-2-0015]
- Direct For Computer & Info Scie & Enginr [1217972] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations [1217972] Funding Source: National Science Foundation
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Crossbar arrays of memristive elements are investigated for the implementation of dictionary learning and sparse coding of natural images. A winner-take-all training algorithm, in conjunction with Oja's rule, is used to learn an over-complete dictionary of feature primitives that resemble Gabor filters. The dictionary is then used in the locally competitive algorithm to form a sparse representation of input images. The impacts of device nonlinearity and parameter variations are evaluated and a compensating procedure is proposed to ensure the robustness of the sparsification. It is shown that, with proper compensation, the memristor crossbar architecture can effectively perform sparse coding with distortion comparable with ideal software implementations at high sparsity, even in the presence of large device-to-device variations in the excess of 100%.
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