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

arXiv: https://arxiv.org/abs/2606.14739 · HTML: https://mlsanity.com/paper/2606.14739/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Meta-Learning Representations for Continual Learning](https://mlsanity.com/paper/1905.12588/) (2019)
- [OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary](https://mlsanity.com/paper/2503.10468/) (2025)
- [When Meta-Learning Meets Online and Continual Learning: A Survey](https://mlsanity.com/paper/2311.05241/) (2023)
- [Decoupled Prototype Learning for Reliable Test-Time Adaptation](https://mlsanity.com/paper/2401.08703/) (2024)
- [Mod-DeepESN: Modular Deep Echo State Network](https://mlsanity.com/paper/1808.00523/) (2018)
- [A 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge](https://mlsanity.com/paper/2410.03414/) (2024)
- [An Energy-efficient Capacitive-RRAM Content Addressable Memory](https://mlsanity.com/paper/2401.09207/) (2024)
- [A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference](https://mlsanity.com/paper/2502.10089/) (2025)
- [An RRAM-Based Implementation of a Template Matching Circuit for Low-Power Analogue Classification](https://mlsanity.com/paper/2303.02651/) (2023)
- [Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation](https://mlsanity.com/paper/2501.01472/) (2025)
- [End-to-End Transformer Acceleration Through Processing-in-Memory Architectures](https://mlsanity.com/paper/2601.14260/) (2025)

## Abstract (arXiv)

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 10MHz.