Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

WoS Article

Memristive electronic synapses are attractive to construct artificial neural networks (ANNs) for neuromorphic computing systems, owing to their excellent electronic performance, high integration density, and low cost. However, the necessity of initializing their conductance through a forming process requires additional peripheral hardware and complex programming algorithms. Herein, the first fabrication of memristors that are initially in low-resistive state (LRS) is reported, which exhibit homogenous initial resistance and switching voltages. When used as electronic synapses in a neuromorphic system to classify images from the CIFAR-10 dataset (Canadian Institute For Advanced Research), the memristors offer x1.83 better throughput per area and consume x0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from approximate to 63% better density and approximate to 17% faster operation. It is demonstrated in the results that tuning the local properties of materials embedded in memristive electronic synapses is an attractive strategy that can lead to an improved neuromorphic performance at the system level.

Memristive electronic synapses are attractive to construct artificial neural networks (ANNs) for neuromorphic computing systems, owing to their excellent electronic performance, high integration density, and low cost. However, the necessity of initializing their conductance through a forming process requires additional peripheral hardware and complex programming algorithms. Herein, the first fabrication of memristors that are initially in low-resistive state (LRS) is reported, which exhibit homogenous initial resistance and switching voltages. When used as electronic synapses in a neuromorphic system to classify images from the CIFAR-10 dataset (Canadian Institute For Advanced Research), the memristors offer x1.83 better throughput per area and consume x0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from approximate to 63% better density and approximate to 17% faster operation. It is demonstrated in the results that tuning the local properties of materials embedded in memristive electronic synapses is an attractive strategy that can lead to an improved neuromorphic performance at the system level.

forming-free devices; low-resistive state; memristors; neuromorphic systems; titanium dioxide

forming-free devices; low-resistive state; memristors; neuromorphic systems; titanium dioxide

ZHU, K.; MAHMOODI, M.; FAHIMI, Z.; XIAO, Y.; WANG, T.; BUKVIŠOVÁ, K.; KOLÍBAL, M.; ROLDÁN, J.; PEREZ, D.; AGUIRRE, F.; LANZA, M.

2022

21.03.2022

Wiley

HOBOKEN

2640-4567

Advanced Intelligent Systems

4

3

Federal Republic of Germany

2200001

220001

9

https://onlinelibrary.wiley.com/doi/10.1002/aisy.202200001

http://hdl.handle.net/11012/204285

2022 (Kolíbal) AISY