Compact Probabilistic Poisson Neuron based on Back-Hopping Oscillation in STT-MRAM for All-Spin Deep Spiking Neural Network
Journal
Digest of Technical Papers - Symposium on VLSI Technology
Journal Volume
2020-June
Date Issued
2020
Author(s)
Abstract
A unique compact Poisson neuron that encodes information in the tunable duty cycle of probabilistic spike trains is presented as an enabling technology for cost-effective spiking neural network (SNN) hardware. The Poisson neuron exploits the back-hopping oscillation (BHO) in scalable spin-transfer torque (STT)-MRAM. The macrospin LLGS simulation confirms that the coupled local Joule heating and STT effects are responsible for the bias-dependent BHO. The complete neuron circuit design is at least 6*smaller than the state-of-the-art integrate-and- fire (IF) CMOS neuron. Hardware-friendly all-spin deep SNNs achieve equivalent accuracy to deep neural networks (DNN), 98.4 % for MNIST, even when considering the probabilistic nature of neurons. ? 2020 IEEE.
Subjects
Cost effectiveness; Deep neural networks; Magnetic recording; Neurons; VLSI circuits; Cost effective; Enabling technologies; Integrate and fires; Neuron circuits; Spiking neural network(SNN); Spiking neural networks; Spin transfer torque; State of the art; Neural networks
SDGs
Type
conference paper