Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction

Abhronil Sengupta, Maryam Parsa, Bing Han, Kaushik Roy

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


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 low-latency 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.

Original languageEnglish (US)
Article number7478636
Pages (from-to)2963-2970
Number of pages8
JournalIEEE Transactions on Electron Devices
Issue number7
StatePublished - Jul 2016

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering


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