Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction

Abstract

Deep Spiking Neural Networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing literature on such computing models are developed with limited insights on the underlying hardware implementation, resulting in area and power expensive designs. Although several neuromimetic devices emulating neural operations have been proposed recently, their functionality has been limited to very simple neural models that may prove to be inefficient at complex recognition tasks. In this work, we venture into the relatively unexplored area of utilizing the inherent device stochasticity of such neuromimetic devices to model complex neural functionalities in a probabilistic framework in the time domain. We consider the implementation of a Deep Spiking Neural Network capable of performing high accuracy and low latency classification tasks where the neural computing unit is enabled by the stochastic switching behavior of a Magnetic Tunnel Junction. Simulation studies indicate an energy improvement of $20\times$ over a baseline CMOS design in $45nm$ technology.

The article will appear in a future issue of IEEE Transactions on Electron Devices

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• Low-Energy Deep Belief Networks using Intrinsic Sigmoidal Spintronic-based Probabilistic Neurons
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• All-Spin Bayesian Neural Networks
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• Design of a Low Voltage Analog-to-Digital Converter using Voltage Controlled Stochastic Switching of Low Barrier Nanomagnets
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• An All-Memristor Deep Spiking Neural Computing System: A Step Towards Realizing the Low Power,Stochastic Brain
• Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference
• SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks