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Hierarchical Temporal Memory Based on Spin-Neurons and Resistive Memory for Energy-Efficient Brain-Inspired Computing (1402.2902v1)
Published 10 Feb 2014 in cs.ET and cond-mat.dis-nn
Abstract: Hierarchical temporal memory (HTM) tries to mimic the computing in cerebral-neocortex. It identifies spatial and temporal patterns in the input for making inferences. This may require large number of computationally expensive tasks like, dot-product evaluations. Nano-devices that can provide direct mapping for such primitives are of great interest. In this work we show that the computing blocks for HTM can be mapped using low-voltage, fast-switching, magneto-metallic spin-neurons combined with emerging resistive cross-bar network (RCN). Results show possibility of more than 200x lower energy as compared to 45nm CMOS ASIC design