Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference.

[1]  Yoshihiko Horio,et al.  Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation , 2016 .

[2]  Werner Scholz,et al.  Micromagnetic simulation of thermally activated switching in fine particles , 2001 .

[3]  Kang L. Wang,et al.  Switching of perpendicular magnetization by spin-orbit torques in the absence of external magnetic fields. , 2013, Nature nanotechnology.

[4]  S. Bandiera,et al.  Perpendicular switching of a single ferromagnetic layer induced by in-plane current injection , 2011, Nature.

[5]  Joshua B. Tenenbaum,et al.  Stochastic Digital Circuits for Probabilistic Inference , 2008 .

[6]  Hyun-Woo Lee,et al.  Spin Hall torque magnetometry of Dzyaloshinskii domain walls , 2013, 1308.1432.

[7]  Goh Wang Ling,et al.  PCMOS-based Hardware Implementation of Bayesian Network , 2007, 2007 IEEE Conference on Electron Devices and Solid-State Circuits.

[8]  Kaushik Roy,et al.  Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons , 2015, Scientific Reports.

[9]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[10]  Jean-Philippe Diguet,et al.  FPGA implementation of Bayesian network inference for an embedded diagnosis , 2015, 2015 IEEE Conference on Prognostics and Health Management (PHM).

[11]  Alan F. Murray,et al.  Pulse arithmetic in VLSI neural networks , 1989, IEEE Micro.

[12]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[13]  Tomohiro Taniguchi,et al.  Current driven asymmetric magnetization switching in perpendicularly magnetized CoFeB/MgO heterostructures , 2014, 1410.7473.

[14]  Slonczewski.  Conductance and exchange coupling of two ferromagnets separated by a tunneling barrier. , 1989, Physical review. B, Condensed matter.

[15]  Jacques Droulez,et al.  Bayesian Inference With Muller C-Elements , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[16]  H. Ohno,et al.  Layer thickness dependence of the current-induced effective field vector in Ta|CoFeB|MgO. , 2012, Nature materials.

[17]  Chetan Singh Thakur,et al.  Bayesian Estimation and Inference Using Stochastic Electronics , 2016, Front. Neurosci..

[18]  David Stark.  Prognostics and Health Management (PHM) , 2010 .

[19]  Supriyo Datta,et al.  Intrinsic optimization using stochastic nanomagnets , 2016, Scientific Reports.

[20]  J. Bokor,et al.  Switching of perpendicularly polarized nanomagnets with spin orbit torque without an external magnetic field by engineering a tilted anisotropy , 2014, Proceedings of the National Academy of Sciences.

[21]  Kaushik Roy,et al.  Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction , 2016, IEEE Transactions on Electron Devices.

[22]  S. Parkin,et al.  Chiral spin torque arising from proximity-induced magnetization , 2014, Nature Communications.

[23]  J. Hirsch.  Spin Hall Effect , 1999, cond-mat/9906160.

[24]  Akhilesh R. Jaiswal,et al.  Ising computation based combinatorial optimization using spin-Hall effect (SHE) induced stochastic magnetization reversal , 2017 .

[25]  John Wawrzynek,et al.  High-throughput bayesian computing machine with reconfigurable hardware , 2010, FPGA '10.

[26]  W. Marsden.  I and J , 2012 .

[27]  D. Ralph,et al.  Spin transfer torque devices utilizing the giant spin Hall effect of tungsten , 2012, 1208.1711.

[28]  D. Ralph,et al.  Spin-Torque Switching with the Giant Spin Hall Effect of Tantalum , 2012, Science.

[29]  Lakhmi C. Jain,et al.  Introduction to Bayesian Networks , 2008 .

[30]  Kaushik Roy,et al.  Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning , 2016, Scientific Reports.

[31]  G. Beach,et al.  Current-driven dynamics of chiral ferromagnetic domain walls. , 2013, Nature materials.

[32]  S. Parkin,et al.  Chiral spin torque at magnetic domain walls. , 2013, Nature nanotechnology.

[33]  D. Ralph,et al.  Current-induced switching of perpendicularly magnetized magnetic layers using spin torque from the spin Hall effect. , 2012, Physical review letters.

[34]  Przemyslaw Mroszczyk,et al.  The accuracy and scalability of continuous-time Bayesian inference in analogue CMOS circuits , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).