Exploiting the Electrothermal Timescale in PrMnO3 RRAM for a compact, clock-less neuron exhibiting biological spiking patterns

TL;DR

This paper introduces a novel PrMnO3 RRAM-based neuron that uses electrothermal timescales to emulate biological spiking patterns, offering a compact, hardware-efficient alternative for large-scale neuromorphic systems.

Contribution

It presents a new neuron circuit using PrMnO3 RRAM devices with electrothermal timescales, replacing large capacitors and enabling realistic biological spiking behaviors.

Findings

Successfully modeled thermal device dynamics with Verilog-A.

Designed circuitry to mimic cortical neuron spiking patterns.

Demonstrated asynchronous neuron behavior through simulation and characterization.

Abstract

Spiking Neural Networks (SNNs) are gaining widespread momentum in the field of neuromorphic computing. These network systems integrated with neurons and synapses provide computational efficiency by mimicking the human brain. It is desired to incorporate the biological neuronal dynamics, including complex spiking patterns which represent diverse brain activities within the neural networks. Earlier hardware realization of neurons was (1) area intensive because of large capacitors in the circuit design, (2) neuronal spiking patterns were demonstrated with clocked neurons at the device level. To achieve more realistic biological neuron spiking behavior, emerging memristive devices are considered promising alternatives. In this paper, we propose, PrMnO3(PMO) -RRAM device-based neuron. The voltage-controlled electrothermal timescales of the compact PMO RRAM device replace the electrical timescales of charging a large capacitor. The electrothermal timescale is used to implement an integration block with multiple voltage-controlled timescales coupled with a refractory block to generate biological neuronal dynamics. Here, first, a Verilog-A implementation of the thermal device model is demonstrated, which captures the current-temperature dynamics of the PMO device. Second, a driving circuitry is designed to mimic different spiking patterns of cortical neurons, including Intrinsic bursting (IB) and Chattering (CH). Third, a neuron circuit model is simulated, which includes the PMO RRAM device model and the driving circuitry to demonstrate the asynchronous neuron behavior. Finally, a hardware-software hybrid analysis is done in which the PMO RRAM device is experimentally characterized to mimic neuron spiking dynamics. The work presents a realizable and more biologically comparable hardware-efficient solution for large-scale SNNs.