Analog Signal Processing Using Stochastic Magnets

We present a low energy-barrier magnet based compact hardware unit for analog stochastic neurons (ASNs) and demonstrate its use as a building-block for neuromorphic hardware. Networks assembled from these units are particularly suited for temporal inferencing and pattern recognition. We demonstrate example applications of these ASNs including multi-layer perceptrons, convolutional neurons, and reservoir computers showing tasks such as temporal sequence learning, processing, and prediction tasks which prove that these units can be used to build efficient, scalable, and adaptive neural network based signal-processors. We also provide an illustrative comparison with digital CMOS based circuits that implement similar functionality with networks built using the presented units, demonstrating a possible two orders of magnitude reduction in component-count and concomitant increase in energy efficiency. Efficient non von-Neumann hardware implementation of such signal-processors can open up a pathway for integration of hardware based cognition in a wide variety of emerging systems such as IoT, industrial controls, bio- and photo-sensors, self-driving automotives, and unmanned aerial vehicles.