An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers

• URL: http://arxiv.org/abs/2606.14739v1
• Date: Tue, 02 Jun 2026 15:08:11 GMT
• Title: An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers
• Abstract summary: This paper demonstrates an artificial neural network (ANN) design leveraging Metal-Oxide Resistive RAM (RRAM)<n>The proposed design is based on a custom template piXeL (TXL) cell used for building the ACAM module.<n>This hardware engine enables on-the-fly learning, where the receptive field parameters can be tuned to track domain shift.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deployment of modern machine learning (ML) solutions on resource-constrained edge devices highlights implementation challenges. This is especially true for extreme edge applications that include safety-critical components, such as autonomous navigation tasks. This paper demonstrates an artificial neural network (ANN) design leveraging Metal-Oxide Resistive RAM (RRAM) -based Analogue Content Addressable Memory (ACAM) as an efficient hardware substrate for performing metric-based classification and online adaptation on the edge. The proposed design is based on a custom Template piXeL (TXL) cell used for building the ACAM module, where each TXL cell acts as a configurable receptive field neuron. These cells employ a Radial Basis activation function to calculate the distance of an input from the programmed receptive field. The TXL can be organised into dense arrays for calculating the distance of a high-dimensional input against all stored prototypes, effectively performing fast and energy efficient similarity search. This hardware engine enables on-the-fly learning, where the receptive field parameters can be tuned to track domain shift. Through simulation of the proposed TXL-RBF classifier we can achieve 89.1\% accuracy on the MNIST dataset while consuming 185fJ per cell per operation when operating at 100MHz.

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