Backhopping-based STT-MRAM Poisson Spiking Neuron for Neuromorphic Computation

• Published in: 2023 IEEE International Reliability Physics Symposium (IRPS) pp. 1 - 6
• Main Authors: Tan, J., Lim, J.H., Kwon, J.H., Naik, V.B., Raghavan, N., Pey, K.L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2023
• Subjects: Backhopping; Behavioral sciences; Communication channels; Computational modeling; Mathematical models; MIMICs; Neuromorphic; Neurons; Poisson; Sociology; Spiking Neural Network; STT-MRAM
• ISSN: 1938-1891
• DOI: 10.1109/IRPS48203.2023.10118343

Spin-transfer-torque magnetic random-access memory (STT-MRAM) is a proven technology for embedded non-volatile memory applications. The backhopping phenomena in STT-MRAM, whereby the resistance of the device oscillates under higher current, has been recently explored for emerging spiking neural network applications. We report a detailed characterization of backhopping in foundry compatible STT-MRAM having ~15kb bit-cell arrays by analyzing the behavior of backhopping spike rate versus applied current and temperature. Our study shows that the backhopping in STT-MRAM exhibits the Poisson statistics with a controllable spike rate with current that displays three regimes: non-backhopping, exponential and linear. This mimics the behavior of a rectified linear unit (ReLU) neuron, a commonly used activation function in deep learning models. A spiking neural network (SNN) communication channel is simulated using the derived statistics and a first principles mathematical framework to analyze the reliability performance of backhopping-based SNN in terms of trading-off the accuracy and applied current.