Abstract
A binarized spiking neural network using auto-reset leaky integrate-and-fire neurons with a two-transistor and three-magnetic tunnel junction core and large signal synapses with two-transistor and two-magnetic tunnel junctions is designed. The network is applied to a classifier of the MNIST handwritten digit dataset with a 784 × 400 synapse crossbar array. The weights are trained offline using the spike-timing-dependent plasticity learning algorithm and deployed to the spin-transfer torque magnetic tunnel junction (STT-MTJ) resistances in the synapses after being binarized. Its performance is evaluated by HSPICE using the STT-MTJ device model, which takes the stochastic change in the angle between the two magnetic moments in the free and pinned layers into consideration. 75% test accuracy is achieved for 1200 patterns with 1 ns read and 1 ns write operations and 0.23 pJ/SOP energy consumption.
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