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This paper analyzes the impact of volatile memristor characteristics (decay rate, quantization, variability) on the performance of Parallel Delayed Feedback Network (PDFN) reservoir computing architectures for image classification. They explore preprocessing methods to improve data representation within the reservoir. The study demonstrates 95.89% accuracy on MNIST, comparable to state-of-the-art memristor-based RC, and maintains 94.2% accuracy under 20% device variability, highlighting the potential of volatile memristors for neuromorphic computing.
Volatile memristors can achieve state-of-the-art image classification accuracy in reservoir computing, even with significant device variability, suggesting they are a viable alternative to traditional CMOS.
Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor-based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time-series prediction and image recognition. Although RC has been demonstrated with several memristive devices, a comprehensive evaluation of device-level requirements remains limited. In this paper, we analyze and explain the operation of a parallel delayed feedback network (PDFN) RC architecture with volatile memristors, focusing on how device characteristics -- such as decay rate, quantization, and variability -- affect reservoir performance. We further discuss strategies to improve data representation in the reservoir using preprocessing methods and suggest potential improvements. The proposed approach achieves 95.89% classification accuracy on MNIST, comparable with the best reported memristor-based RC implementations. Furthermore, the method maintains high robustness under 20% device variability, achieving an accuracy of up to 94.2%. These results demonstrate that volatile memristors can support reliable spatio-temporal information processing and reinforce their potential as key building blocks for compact, high-speed, and energy-efficient neuromorphic computing systems.
