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Memristors learn to play

Journal Article · · Nature Electronics
 [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

A hybrid analogue–digital computing system built upon memristive devices is capable of solving classic control problems with potentially a lower energy consumption and higher speed than fully digital systems.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1575274
Report Number(s):
SAND-2019-3025J; 673542
Journal Information:
Nature Electronics, Vol. 2, Issue 3; ISSN 2520-1131
Publisher:
Springer NatureCopyright Statement
Country of Publication:
United States
Language:
English

References (7)

Training deep neural networks for binary communication with the Whetstone method journal January 2019
Training and operation of an integrated neuromorphic network based on metal-oxide memristors journal May 2015
Reinforcement learning with analogue memristor arrays journal March 2019
Computing with Spikes: The Advantage of Fine-Grained Timing journal October 2018
Mastering the game of Go without human knowledge journal October 2017
Human-level control through deep reinforcement learning journal February 2015
A million spiking-neuron integrated circuit with a scalable communication network and interface journal August 2014

Cited By (1)

Nanosystems, Edge Computing, and the Next Generation Computing Systems journal September 2019