Abstract
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 overcomplete 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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