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Unconventional computing with memristive devices and arrays

Memristive devices have become a promising candidate for unconventional computing. In this talk, I will present some of our recent work on unconventional computing experimentally implemented by using memristive devices or crossbar arrays.  Using traditional non-volatile memristors with 64 stable analog resistance levels, we have built a dot-product engine based on a 128 x 64 1T1R crossbar array. Accurate image compression and filtering have been demonstrated with such analog computing accelerator. In addition, we have demonstrated efficient and self-adaptive in-situ learning in a two-layer neural networks using such memristive arrays, which is expected to significantly improve the speed and energy efficiency of deep neural networks.

Using our newly developed diffusive memristors with diffusion dynamics that is critical for neuromorphic functions, we have developed artificial synapses and neurons to more faithfully emulate their bio-counterparts and more efficiently perform spiking neural network functions. We have further integrated these artificial synapses and neurons into a small neural network, with which pattern classification and unsupervised learning have been demonstrated. Moreover, the diffusive memristors can be used as true random number generators for cybersecurity applications and artificial nociceptors for robotics applications. 

J. Joshua Yang

Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA

Created March 21, 2018, Updated October 1, 2018