Hierarchical Temporal Memory Based on Spin-Neurons and Resistive Memory for Energy-Efficient Brain-Inspired Computing
Emerging Technologies
2016-11-17 v1 Disordered Systems and Neural Networks
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
Cite
@article{arxiv.1402.2902,
title = {Hierarchical Temporal Memory Based on Spin-Neurons and Resistive Memory for Energy-Efficient Brain-Inspired Computing},
author = {Deliang Fan and Mrigank Sharad and Abhronil Sengupta and Kaushik Roy},
journal= {arXiv preprint arXiv:1402.2902},
year = {2016}
}
Comments
this work was submitted to IEEE Transactions on Neural Networks and Learning Systems. It is under review now