Memristive devices¹ have become a promising candidate for unconventional computing². 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 array³. Accurate image compression and filtering have been demonstrated with such analog computing accelerator³. In addition, we have demonstrated efficient and self-adaptive in-situ learning in a two-layer neural networks using such memristive arrays⁴, which is expected to significantly improve the speed and energy efficiency of deep neural networks.

Using our newly developed diffusive memristors⁵ with diffusion dynamics that is critical for neuromorphic functions, we have developed artificial synapses⁶ and neurons⁷ 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 demonstrated⁷. Moreover, the diffusive memristors can be used as true random number generators⁸ for cybersecurity applications and artificial nociceptors for robotics applications⁹.

1. Yang, J. J. et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nature Nanotechnology 3, 429 (2008).

2. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nature Nanotechnology 8, 13 (2013).

3. Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nature Electronics 1, 52 (2018).

4. Li, C. et al. Efficient and self-adaptive in-situ learning in multilayer memristive neural networks. Nature communications, under revision (2018).

5. Midya, R. et al. Anatomy of Ag/Hafnia-Based Selectors with 1E10 Nonlinearity. Advanced Materials 29, 1604457 (2017).

6. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nature Materials 16, 101-108 (2017).

7. Wang, Z. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nature Electronics 1, 137 (2018).

8. Jiang, H. et al. A Novel True Random Number Generator Based on a Stochastic Diffusive Memristor. Nature communications 8, 882 (2017).

9. Yoon, J. H. et al. An artificial nociceptor based on a diffusive memristor. Nature Communications 9, 417 (2018).