TY - GEN
T1 - Stochastic spintronic device based synapses and spiking neurons for neuromorphic computation
AU - Zhang, Deming
AU - Zeng, Lang
AU - Zhang, Youguang
AU - Zhao, Weisheng
AU - Klein, Jacques Olivier
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/9/14
Y1 - 2016/9/14
N2 - Spintronics devices such as magnetic tunnel junction (MTJ) have been investigated for the neuromorphic computation. However, there are still a number of challenges for hardware implementation of the bio-inspired computing, for instance how to use the binary MTJ to mimic the analog synapse. In this paper, a compound scheme is firstly proposed, which employs multiple MTJs connected in parallel operating in the stochastic regime to jointly behave a single synapse, aiming to achieve an analog-like weight spectrum. To further exploit its stochastic switching property for the bio-inspired computing, we present a MTJ based stochastic spiking neuron (SSN) circuit, which can also realize the neural rate coding scheme. A case study is made on the MNIST database for handwritten digital recognition with the proposed compound magnetoresistive synapse (CMS) and SSN. System-level simulation results show that the proposed CMS and SSN can implement neuromorphic computation with high accuracy and immunity to device variation.
AB - Spintronics devices such as magnetic tunnel junction (MTJ) have been investigated for the neuromorphic computation. However, there are still a number of challenges for hardware implementation of the bio-inspired computing, for instance how to use the binary MTJ to mimic the analog synapse. In this paper, a compound scheme is firstly proposed, which employs multiple MTJs connected in parallel operating in the stochastic regime to jointly behave a single synapse, aiming to achieve an analog-like weight spectrum. To further exploit its stochastic switching property for the bio-inspired computing, we present a MTJ based stochastic spiking neuron (SSN) circuit, which can also realize the neural rate coding scheme. A case study is made on the MNIST database for handwritten digital recognition with the proposed compound magnetoresistive synapse (CMS) and SSN. System-level simulation results show that the proposed CMS and SSN can implement neuromorphic computation with high accuracy and immunity to device variation.
KW - Analog-like Weight Spectrum
KW - Compound Scheme
KW - Magnetic Tunnel Junction (MTJ)
KW - Neuromorphic Computation
KW - Spintronics devices
KW - Stochastic Spiking Neuron (SSN)
UR - https://www.scopus.com/pages/publications/84992134031
U2 - 10.1145/2950067.2950105
DO - 10.1145/2950067.2950105
M3 - 会议稿件
AN - SCOPUS:84992134031
T3 - Proceedings of the 2016 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2016
SP - 173
EP - 178
BT - Proceedings of the 2016 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2016
PB - Presses Polytechniques Et Universitaires Romandes
T2 - 2016 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2016
Y2 - 18 July 2016 through 20 July 2016
ER -