Title

A Spin-Orbit Torque Based Cellular Neural Network(Cnn) Architecture

Keywords

Cellular neural network; Domain wall; Spin-orbit torque

Abstract

In this paper, we propose a differential Spin Hall Effect(SHE) assisted domain wall synapse, which can generate either positive or negative synaptic weighting values without the significant cost of multiple power supply voltages, supply rails, or computationally-intensive digital hardware. The architecture of the proposed synapse utilizes reading currents owing through two oppositely-oriented devices as weighted by device conductance. The conductance is used to encode synaptic weight and programmed by domain wall position through writing current. The ability to set the current as positively or negatively weighted results in highly-configurable functionality within a compact synapse design. The synapses are used with a soft-limiting nonlinear neuron to employ the relationship between positions and input current magnitude. We show through micro-magnetic simulation how the non-volatile physical characteristic of the domain wall calibrated synapse is used to implement a numerical integration function to realize a Cellular Neural Network(CNN). The performance of the proposed CNN design for isolated letter denoising at 0ns to 4ns demonstrates noise filtering functionality with total energy consumption during sensing of 24fJ. This compares favorably to existing spin CNN cell designs to provide a promising design approach for intrinsic neural computation.

Publication Date

5-10-2017

Publication Title

Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Volume

Part F127756

Number of Pages

59-64

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3060403.3060472

Socpus ID

85021213459 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85021213459

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