RT info:eu-repo/semantics/article
T1 Multilevel HfO2-based RRAM devices for low-power neuromorphic networks
A1 Milo, V.
A1 Zambelli, Cristian
A1 Olivo, P.
A1 Pérez, Eduardo
A1 Mahadevaiah, M. K.
A1 González Ossorio, Óscar
A1 Wenger, Christian
A1 Ielmini, Daniele
K1 Metal oxides
K1 Óxidos metálicos
K1 Artificial neural networks
K1 Redes neuronales artificiales
K1 Analog computation
K1 Computación analógica
AB Training and recognition with neural networks generally require high throughput, high energy efficiency, and scalable circuits to enable artificial intelligence tasks to be operated at the edge, i.e., in battery-powered portable devices and other limited-energy environments. In this scenario, scalable resistive memories have been proposed as artificial synapses thanks to their scalability, reconfigurability, and high-energy efficiency, and thanks to the ability to perform analog computation by physical laws in hardware. In this work, we study the material, device, and architecture aspects of resistive switching memory (RRAM) devices for implementing a 2-layer neural network for pattern recognition. First, various RRAM processes are screened in view of the device window, analog storage, and reliability. Then, synaptic weights are stored with 5-level precision in a 4 kbit array of RRAM devices to classify the Modified National Institute of Standards and Technology (MNIST) dataset. Finally, classification performance of a 2-layer neural network is tested before and after an annealing experiment by using experimental values of conductance stored into the array, and a simulation-based analysis of inference accuracy for arrays of increasing size is presented. Our work supports material-based development of RRAM synapses for novel neural networks with high accuracy and low-power consumption.
PB AIP Publishing
SN 2166-532X
YR 2019
FD 2019
LK http://uvadoc.uva.es/handle/10324/44670
UL http://uvadoc.uva.es/handle/10324/44670
LA eng
NO APL Materials, 2019, vol. 7, n. 8. 10 p.
NO Producción Científica
DS UVaDOC
RD 24-nov-2024