Daniel Belkin

TL;DR: This work monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer memristor neural network and achieves competitive classification accuracy on a standard machine learning dataset.

TL;DR: A novel true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity, and power consumption.

TL;DR: It is demonstrated experimentally that the synaptic weights shared in different time steps in an LSTM can be implemented with a memristor crossbar array, which has a small circuit footprint, can store a large number of parameters and offers in-memory computing capability that contributes to circumventing the ‘von Neumann bottleneck’.

TL;DR: An experimental demonstration of reinforcement learning on a three-layer 1-transistor 1-memristor (1T1R) network using a modified learning algorithm tailored for the authors' hybrid analogue–digital platform, which has the potential to achieve a significant boost in speed and energy efficiency.

TL;DR: Yang et al. as discussed by the authors demonstrate that LSTM can be implemented with a memristor crossbar, which has a small circuit footprint to store a large number of parameters and in-memory computing capability that circumvents thevon Neumann bottleneck.