Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition

TL;DR

This paper introduces a novel RTD-based reservoir computing system designed for efficient image recognition, demonstrating promising results on digit and object classification tasks suitable for resource-constrained environments.

Contribution

The study presents a hardware-efficient, RTD-based neuromorphic architecture for reservoir computing, combining theoretical formulation and numerical validation for image recognition.

Findings

Effective on handwritten digit classification

Successful object recognition with Fruit 360 dataset

Hardware-efficient design suitable for edge computing

Abstract

As artificial intelligence continues to push into real-time, edge-based and resource-constrained environments, there is an urgent need for novel, hardware-efficient computational models. In this study, we present and validate a neuromorphic computing architecture based on resonant-tunnelling diodes (RTDs), which exhibit the nonlinear characteristics ideal for physical reservoir computing (RC). We theoretically formulate and numerically implement an RTD-based RC system and demonstrate its effectiveness on two image recognition benchmarks: handwritten digit classification and object recognition using the Fruit~360 dataset. Our results show that this circuit-level architecture delivers promising performance while adhering to the principles of next-generation RC -- eliminating random connectivity in favour of a deterministic nonlinear transformation of input signals.