On-chip learning for domain wall synapse based Fully Connected Neural Network
This work addresses hardware implementation of neuromorphic systems for data classification tasks, but it is incremental as it builds on existing spintronic and analog circuit methods.
The authors designed a fully connected neural network using spintronic domain wall synapses and analog transistor neurons, and demonstrated on-chip training on MNIST with reported training and test accuracies and energy consumption.
Spintronic devices are considered as promising candidates in implementing neuromorphic systems or hardware neural networks, which are expected to perform better than other existing computing systems for certain data classification and regression tasks. In this paper, we have designed a feedforward Fully Connected Neural Network (FCNN) with no hidden layer using spin orbit torque driven domain wall devices as synapses and transistor based analog circuits as neurons. A feedback circuit is also designed using transistors, which at every iteration computes the change in weights of the synapses needed to train the network using Stochastic Gradient Descent (SGD) method. Subsequently it sends write current pulses to the domain wall based synaptic devices which move the domain walls and updates the weights of the synapses. Through a combination of micromagnetic simulations, analog circuit simulations and numerically solving FCNN training equations, we demonstrate "on-chip" training of the designed FCNN on the MNIST database of handwritten digits in this paper. We report the training and test accuracies, energy consumed in the synaptic devices for the training and possible issues with hardware implementation of FCNN that can limit its test accuracy.