TY - GEN
T1 - Magnetic tunnel junction enabled all-spin stochastic spiking neural network
AU - Srinivasan, Gopalakrishnan
AU - Sengupta, Abhronil
AU - Roy, Kaushik
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/11
Y1 - 2017/5/11
N2 - Biologically-inspired spiking neural networks (SNNs) have attracted significant research interest due to their inherent computational efficiency in performing classification and recognition tasks. The conventional CMOS-based implementations of large-scale SNNs are power intensive. This is a consequence of the fundamental mismatch between the technology used to realize the neurons and synapses, and the neuroscience mechanisms governing their operation, leading to area-expensive circuit designs. In this work, we present a three-terminal spintronic device, namely, the magnetic tunnel junction (MTJ)-heavy metal (HM) heterostructure that is inherently capable of emulating the neuronal and synaptic dynamics. We exploit the stochastic switching behavior of the MTJ in the presence of thermal noise to mimic the probabilistic spiking of cortical neurons, and the conditional change in the state of a binary synapse based on the pre- and post-synaptic spiking activity required for plasticity. We demonstrate the efficacy of a crossbar organization of our MTJ-HM based stochastic SNN in digit recognition using a comprehensive device-circuit-system simulation framework. The energy efficiency of the proposed system stems from the ultra-low switching energy of the MTJ-HM device, and the in-memory computation rendered possible by the localized arrangement of the computational units (neurons) and non-volatile synaptic memory in such crossbar architectures.
AB - Biologically-inspired spiking neural networks (SNNs) have attracted significant research interest due to their inherent computational efficiency in performing classification and recognition tasks. The conventional CMOS-based implementations of large-scale SNNs are power intensive. This is a consequence of the fundamental mismatch between the technology used to realize the neurons and synapses, and the neuroscience mechanisms governing their operation, leading to area-expensive circuit designs. In this work, we present a three-terminal spintronic device, namely, the magnetic tunnel junction (MTJ)-heavy metal (HM) heterostructure that is inherently capable of emulating the neuronal and synaptic dynamics. We exploit the stochastic switching behavior of the MTJ in the presence of thermal noise to mimic the probabilistic spiking of cortical neurons, and the conditional change in the state of a binary synapse based on the pre- and post-synaptic spiking activity required for plasticity. We demonstrate the efficacy of a crossbar organization of our MTJ-HM based stochastic SNN in digit recognition using a comprehensive device-circuit-system simulation framework. The energy efficiency of the proposed system stems from the ultra-low switching energy of the MTJ-HM device, and the in-memory computation rendered possible by the localized arrangement of the computational units (neurons) and non-volatile synaptic memory in such crossbar architectures.
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U2 - 10.23919/DATE.2017.7927045
DO - 10.23919/DATE.2017.7927045
M3 - Conference contribution
AN - SCOPUS:85020202697
T3 - Proceedings of the 2017 Design, Automation and Test in Europe, DATE 2017
SP - 530
EP - 535
BT - Proceedings of the 2017 Design, Automation and Test in Europe, DATE 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th Design, Automation and Test in Europe, DATE 2017
Y2 - 27 March 2017 through 31 March 2017
ER -