Neuromorphic computing with memristive devices

Abstract

Technology advances in the last a few decades have resulted in profound changes in our society, from workplaces to living rooms to how we socialize with each other. These changes in turn drive further technology developments, as the exponential growth of data demands ever increasing computing power. However, improvements in computing capacity from device scaling alone is no longer sufficient, and new materials, devices, and architectures likely need to be developed collaboratively to meet present and future computing needs. Specifically, devices that offer co-located memory and computing characteristics, as represented by memristor devices and memristor-based computing systems, have attracted broad interest in the last decade. Besides tremendous appeal in data storage applications, memristors offer the potential for efficient hardware realization of neuromorphic computing architectures that can effectively address the memory and energy walls faced by conventional von Neumann computing architectures. In this review, we evaluate the state-of-the-art in memristor devices and systems, and highlight the potential and challenges of applying such devices and architectures in neuromorphic computing applications. New directions that can lead to general, efficient in-memory computing systems will also be discussed.

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Acknowledgements

This work was supported in part by National Science Foundation (NSF) (Grant Nos. ECCS-1708700, CCF-1617315). We would like to thank F CAI, J LEE and J SHIN for helpful discussions.

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Correspondence to Wei D. Lu.

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Ma, W., Zidan, M.A. & Lu, W.D. Neuromorphic computing with memristive devices. Sci. China Inf. Sci. 61, 060422 (2018). https://doi.org/10.1007/s11432-017-9424-y

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Keywords

  • memristor
  • resistive random-access-memory (RRAM)
  • neuromorphic computing
  • non-von Neumann
  • process in-memory