Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction

Deep spiking neural networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing studies on such computing models are developed with limited insights on the underlying hardware implementation, resulting in area and power expensive designs. Althou...

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Bibliographic Details
Published in:IEEE transactions on electron devices 2016-07, Vol.63 (7), p.2963-2970
Main Authors: Sengupta, Abhronil, Parsa, Maryam, Bing Han, Roy, Kaushik
Format: Article
Language:English
Subjects:
Artificial neural networks
Computation
Computational modeling
Computer simulation
Design engineering
Devices
Magnetic tunnel junction (MTJ)
Magnetic tunneling
Neural networks
neuromorphic computing
Neurons
Probabilistic logic
Probability theory
Spiking
spiking neural networks
Stochasticity
Switches
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Summary:Deep spiking neural networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing studies 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 paper, 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 lowlatency classification tasks, where the neural computing unit is enabled by the stochastic switching behavior of a magnetic tunnel junction. The simulation studies indicate an energy improvement of 20× over a baseline CMOS design in 45-nm technology.
ISSN:0018-9383
1557-9646
DOI:10.1109/TED.2016.2568762