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Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithmthat holds promise for invariant representations of spatial and spatiotemporalinputs. This paper presents a comprehensive neuromemristive crossbararchitecture for the spatial pooler (SP) and the sparse distributedrepresentation classifier, which are fundamental to the algorithm. There areseveral unique features in the proposed architecture that tightly link with theHTM algorithm. A memristor that is suitable for emulating the HTM synapses isidentified and a new Z-window function is proposed. The architecture exploitsthe concept of synthetic synapses to enable potential synapses in the HTM. Thecrossbar for the SP avoids dark spots caused by unutilized crossbar regions andsupports rapid on-chip training within 2 clock cycles. This research alsoleverages plasticity mechanisms such as neurogenesis and homeostatic intrinsicplasticity to strengthen the robustness and performance of the SP. The proposeddesign is benchmarked for image recognition tasks using MNIST and Yale facesdatasets, and is evaluated using different metrics including entropy,sparseness, and noise robustness. Detailed power analysis at different stagesof the SP operations is performed to demonstrate the suitability for mobileplatforms.

Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis