STT-BSNN: An In-Memory Deep Binary Spiking Neural Network Based on STT-MRAM

STT-BSNN: An In-Memory Deep Binary Spiking Neural Network Based on STT-MRAM

This paper proposes an in-memory binary spiking neural network (BSNN) based on spin-transfer-torque magnetoresistive RAM (STT-MRAM). We propose residual BSNN learning using a surrogate gradient that shortens the time steps in the BSNN while maintaining sufficient accuracy. At the circuit level, pres...

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Bibliographic Details
Main Authors: Van-Tinh Nguyen, Quang-Kien Trinh, Renyuan Zhang, Yasuhiko Nakashima
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Binary spiking neural network
emerging memory technology
in-memory computing
neuromorphic computing
process variation
STT-MRAM
Online Access:https://ieeexplore.ieee.org/document/9605611/

Internet

https://ieeexplore.ieee.org/document/9605611/

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