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arXiv · 1812.08273

Analog Signal Processing Using Stochastic Magnets

Samiran Ganguly·Kerem Y. Camsari·Avik W. Ghosh
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Abstract

We present a low barrier magnet based compact hardware unit for analog stochastic neurons and demonstrate its use as a building-block for neuromorphic hardware. By coupling circular magnetic tunnel junctions (MTJs) with a CMOS based analog buffer, we show that these units can act as leaky-integrate-and fire (LIF) neurons, a model of biological neural networks particularly suited for temporal inferencing and pattern recognition. We demonstrate examples of temporal sequence learning, processing, and prediction tasks in real time, as a proof of concept demonstration of scalable and adaptive signal-processors. Efficient non von-Neumann hardware implementation of such 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, and Unmanned Autonomous Vehicles.

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Samiran Ganguly, Kerem Y. Camsari, Avik W. Ghosh. 2018-12-19. Analog Signal Processing Using Stochastic Magnets. https://doi.org/10.1109/access.2021.3075839

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