Ultra-low power probabilistic IMT neurons for stochastic sampling machines

Stochastic sampling machines (SSM) utilize neural sampling from probabilistic spiking neurons to escape local minima and prevent overfitting of training datasets [1]. This enables improved error rates compared to deterministic implementations, and, in turn, enables lower bit precision, decreased chip area, and reduced energy consumption. In this work, we experimentally demonstrate: (i) Insulator-to-Metal Phase Transition (IMT) neurons with record low peak operating power of 11.9μW at V DD =0.7V; (ii) the IMT in vanadium dioxide (VO2) provides a natural probabilistic hardware substrate for realizing a compact stochastic IMT neuron for SSMs; (iii) implementation of SSM for pattern recognition on MNIST database [2] using experimentally calibrated device modeling. These results are compared to a 22nm CMOS ASIC which shows stochastic IMT neuron based SSMs result in a 4.5x reduction in system power consumption.