aiqbench analysis
This paper presents a novel hardware architecture using RRAM-based ACAM to implement Radial Basis Function neurons specifically for energy-efficient edge classification and online adaptation. By leveraging analogue content addressable memory, the design achieves high accuracy on standard benchmarks while enabling fast similarity search and tracking of domain shifts in safety-critical scenarios. The work bridges the gap between theoretical neural network models and practical, low-power hardware implementations for autonomous systems.
An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers
arXiv:2606.14739v2 Announce Type: replace-cross 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 10MHz.
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 10MHz.
Emerging Technologies (cs.ET); Machine Learning (cs.LG); Systems and Control (eess.SY)
arXiv:2606.14739 [cs.ET]
(or arXiv:2606.14739v2 [cs.ET] for this version)
https://doi.org/10.48550/arXiv.2606.14739 Focus to learn more arXiv-issued DOI via DataCite
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