Computer hardware

Memristor

Voltage

Engineering

Non-volatile memory

Efficient energy use

Materials science

Telecommunications

Power (physics)

Scalability

Computer science

Thermodynamics

Physics

Nanotechnology

Computer architecture

Database

MNIST database

Crossbar switch

Neuromorphic engineering

Electronic engineering

Ultra low power

Artificial intelligence

Reconfigurability

Resistive random-access memory

Artificial neural network

Power consumption

Optoelectronics

Electrical engineering

Engineering physics

APL Materials

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.

Multilevel HfO2-based RRAM devices for low-power neuromorphic networks