Shape-Based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems. Previous proposals for spintronic artificial neurons required external stimuli to perform the leaking functionality, one of the three fundamental functions of a leaky integrate-and-fire (LIF) neuron. The use of this external magnetic field or electrical current stimulus results in either a decrease in energy efficiency or an increase in fabrication complexity. In this article, we modify the shape of previously demonstrated three-terminal magnetic tunnel junction neurons to perform the leaking operation without any external stimuli. The trapezoidal structure causes a shape-based DW drift, thus intrinsically providing the leaking functionality with no hardware cost. This LIF neuron, therefore, promises to advance the development of spintronic neural network crossbar arrays.

[1]  Christopher H. Bennett,et al.  Graded-Anisotropy-Induced Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron , 2019, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.

[2]  Joseph S. Friedman,et al.  Magnetic domain wall neuron with lateral inhibition , 2018, Journal of Applied Physics.

[3]  Xing Chen,et al.  A compact skyrmionic leaky-integrate-fire spiking neuron device. , 2018, Nanoscale.

[4]  Caroline A. Ross,et al.  A logic-in-memory design with 3-terminal magnetic tunnel junction function evaluators for convolutional neural networks , 2017, 2017 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH).

[5]  Joseph S. Friedman,et al.  Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing , 2017, 1706.05262.

[6]  Abhronil Sengupta,et al.  A Vision for All-Spin Neural Networks: A Device to System Perspective , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[7]  Kaushik Roy,et al.  Proposal for a Leaky-Integrate-Fire Spiking Neuron Based on Magnetoelectric Switching of Ferromagnets , 2016, IEEE Transactions on Electron Devices.

[8]  Jacques-Olivier Klein,et al.  Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses , 2016, Scientific Reports.

[9]  C. A. Ross,et al.  Logic circuit prototypes for three-terminal magnetic tunnel junctions with mobile domain walls , 2016, Nature Communications.

[10]  Caroline A. Ross,et al.  Micromagnetic modeling of domain wall motion in sub-100-nm-wide wires with individual and periodic edge defects , 2015 .

[11]  Kaushik Roy,et al.  Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[12]  Song Han,et al.  Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.

[13]  Bernard Brezzo,et al.  TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip , 2015, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[14]  Vijay Balasubramanian,et al.  Heterogeneity and Efficiency in the Brain , 2015, Proceedings of the IEEE.

[15]  Kaushik Roy,et al.  Spin-Transfer Torque Magnetic neuron for low power neuromorphic computing , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[16]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[17]  F. García-Sánchez,et al.  The design and verification of MuMax3 , 2014, 1406.7635.

[18]  Martin Stemmler,et al.  Power Consumption During Neuronal Computation , 2014, Proceedings of the IEEE.

[19]  Jacques-Olivier Klein,et al.  Bioinspired networks with nanoscale memristive devices that combine the unsupervised and supervised learning approaches , 2012, 2012 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH).

[20]  C. Ross,et al.  Low Energy Magnetic Domain Wall Logic in Short, Narrow, Ferromagnetic Wires , 2012, IEEE Magnetics Letters.

[21]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[22]  C. Chappert,et al.  Influence of geometry on domain wall propagation in a mesoscopic wire , 2001 .

[23]  Jacques Gautrais,et al.  SpikeNET: A simulator for modeling large networks of integrate and fire neurons , 1999, Neurocomputing.

[24]  Mrigank Sharad,et al.  Energy-Efficient Non-Boolean Computing With Spin Neurons and Resistive Memory , 2014, IEEE Transactions on Nanotechnology.