HackerPapers

arXiv:1402.2902cs.ET

Hierarchical Temporal Memory Based on Spin-Neurons and Resistive Memory for Energy-Efficient Brain-Inspired Computing

Deliang Fan, Mrigank Sharad, Abhronil Sengupta, Kaushik Roy

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

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

arXiv comments
this work was submitted to IEEE Transactions on Neural Networks and Learning Systems. It is under review now
Journal reference
DOI
doi:10.1109/TNNLS.2015.2462731
Subjects
cs.ETEmerging Technologies cond-mat.dis-nnDisordered Systems and Neural Networks

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Nov 2022 to Nov 2022.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. Study urges caution when comparing neural networks to the brain 1 comment

    • We've been doing significant research using HTM[1][2][3][4] w/ SDR[5] similar to the proprietary implementation of Semantic Folding[6] and were able to classify literally gigabytes of documents per second on FPGAs. HTM aims to resemble the architecture of the Neocortex. I simply…

      19hView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.