Hardware-Aware In Situ Learning Based on Stochastic Magnetic Tunnel Junctions

Jan Kaiser, William A. Borders, Kerem Y. Camsari, ∗ Shunsuke Fukami, 4, 5, 6, 7, † Hideo Ohno, 4, 5, 6, 7 and Supriyo Datta Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47906 USA Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University, Sendai, Japan Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA, 93106 USA Center for Innovative Integrated Electronic Systems, Tohoku University, Sendai, Japan. Center for Spintronics Research Network, Tohoku University, Sendai, Japan. Center for Science and Innovation in Spintronics, Tohoku University, Sendai, Japan. WPI-Advanced Institute for Materials Research, Tohoku University, Sendai, Japan. (Dated: January 17, 2022)

[1]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[2]  Supriyo Datta,et al.  Hardware Design for Autonomous Bayesian Networks , 2020, Frontiers in Computational Neuroscience.

[3]  E. Vianello,et al.  In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling , 2021 .

[4]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[5]  Pritish Narayanan,et al.  Equivalent-accuracy accelerated neural-network training using analogue memory , 2018, Nature.

[6]  Hong Wang,et al.  Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.

[7]  A. Ng Feature selection, L1 vs. L2 regularization, and rotational invariance , 2004, Twenty-first international conference on Machine learning - ICML '04.

[8]  Engin Ipek,et al.  Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning , 2017, 2017 Fifth Berkeley Symposium on Energy Efficient Electronic Systems & Steep Transistors Workshop (E3S).

[9]  Supriyo Datta,et al.  Probabilistic Circuits for Autonomous Learning: A Simulation Study , 2020, Frontiers in Computational Neuroscience.

[10]  Miguel Á. Carreira-Perpiñán,et al.  On Contrastive Divergence Learning , 2005, AISTATS.

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

[12]  Bruce F. Cockburn,et al.  Variation-Resilient True Random Number Generators Based on Multiple STT-MTJs , 2018, IEEE Transactions on Nanotechnology.

[13]  Supriyo Datta,et al.  Implementing p-bits With Embedded MTJ , 2017, IEEE Electron Device Letters.

[14]  Big data needs a hardware revolution , 2018, Nature.

[15]  Kerem Yunus Camsari,et al.  Weighted $p$ -Bits for FPGA Implementation of Probabilistic Circuits , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[16]  M. R. Mahmoodi,et al.  Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization , 2019, Nature Communications.

[17]  Geoffrey E. Hinton,et al.  A Better Way to Pretrain Deep Boltzmann Machines , 2012, NIPS.

[18]  Boulder,et al.  Large-angle, gigahertz-rate random telegraph switching induced by spin-momentum transfer , 2004, cond-mat/0404109.

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

[20]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[21]  Thomas R. Osborn Fast Teaching of Boltzmann Machines with Local Inhibition , 1990 .

[22]  H. Kappen,et al.  An atomic Boltzmann machine capable of self-adaption , 2020, Nature Nanotechnology.

[23]  A. R. Trivedi,et al.  Low Power Restricted Boltzmann Machine Using Mixed-Mode Magneto-Tunneling Junctions , 2019, IEEE Electron Device Letters.

[24]  Nitish Srivastava,et al.  Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..

[25]  Raymond Beausoleil,et al.  Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks , 2020, Nature Electronics.

[26]  Geoffrey E. Hinton,et al.  Implicit Mixtures of Restricted Boltzmann Machines , 2008, NIPS.

[27]  Supriyo Datta,et al.  Voltage-Driven Building Block for Hardware Belief Networks , 2018, IEEE Design & Test.

[28]  Damien Querlioz,et al.  Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines , 2019, Scientific Reports.

[29]  M. Stiles,et al.  Neuromorphic spintronics , 2020, Nature Electronics.

[30]  Supriyo Datta,et al.  Autonomous Probabilistic Coprocessing With Petaflips per Second , 2019, IEEE Access.

[31]  Chung Lam,et al.  Training a Probabilistic Graphical Model With Resistive Switching Electronic Synapses , 2016, IEEE Transactions on Electron Devices.

[32]  W. Coffey,et al.  Thermal fluctuations of magnetic nanoparticles: Fifty years after Brown , 2012, 1209.0298.

[33]  Supriyo Datta,et al.  Implementing Bayesian Networks with Embedded Stochastic MRAM , 2018, ArXiv.

[34]  Kathleen E. Hamilton,et al.  Accelerating Scientific Computing in the Post-Moore’s Era , 2020, ACM Trans. Parallel Comput..

[35]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[36]  Nicolas Le Roux,et al.  Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.

[37]  Supriyo Datta,et al.  Subnanosecond Fluctuations in Low-Barrier Nanomagnets , 2019 .

[38]  J. W. Brown Thermal Fluctuations of a Single-Domain Particle , 1963 .

[39]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[40]  Emile H. L. Aarts,et al.  Boltzmann Machines and their Applications , 1987, PARLE.

[41]  Yang Lv,et al.  Experimental Demonstration of Probabilistic Spin Logic by Magnetic Tunnel Junctions , 2019, IEEE Magnetics Letters.

[42]  Yu Wang,et al.  Technological Exploration of RRAM Crossbar Array for Matrix-Vector Multiplication , 2015, Journal of Computer Science and Technology.

[43]  Andre Esteva,et al.  A guide to deep learning in healthcare , 2019, Nature Medicine.

[44]  Yann LeCun,et al.  The mnist database of handwritten digits , 2005 .

[45]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[46]  R. Salakhutdinov Learning and Evaluating Boltzmann Machines , 2008 .

[47]  Brian M. Sutton,et al.  Stochastic p-bits for Invertible Logic , 2016, 1610.00377.

[48]  Qing Wu,et al.  Efficient and self-adaptive in-situ learning in multilayer memristor neural networks , 2018, Nature Communications.

[49]  Kaiming He,et al.  Rethinking ImageNet Pre-Training , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[50]  R. Feynman Simulating physics with computers , 1999 .

[51]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[52]  Tijmen Tieleman,et al.  Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.

[53]  S. Datta,et al.  Probabilistic Computing with Binary Stochastic Neurons , 2019, 2019 IEEE BiCMOS and Compound semiconductor Integrated Circuits and Technology Symposium (BCICTS).

[54]  Vivienne Sze,et al.  Hardware for machine learning: Challenges and opportunities , 2017, 2017 IEEE Custom Integrated Circuits Conference (CICC).

[55]  Yu Wang,et al.  Memristor-based approximated computation , 2013, International Symposium on Low Power Electronics and Design (ISLPED).

[56]  Supriyo Datta,et al.  Low-Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons , 2019, IEEE Magnetics Letters.

[57]  Marco Lanuzza,et al.  Variability-Aware Analysis of Hybrid MTJ/CMOS Circuits by a Micromagnetic-Based Simulation Framework , 2017, IEEE Transactions on Nanotechnology.

[58]  Supriyo Datta,et al.  Integer factorization using stochastic magnetic tunnel junctions , 2019, Nature.

[59]  Chen-Yi Lee,et al.  A 41.3/26.7 pJ per Neuron Weight RBM Processor Supporting On-Chip Learning/Inference for IoT Applications , 2017, IEEE Journal of Solid-State Circuits.