arXiv：cs.LG· Georgios Papandroulidakis, Shady Agwa, Themis Prodromakis·· 5 小时前AI 评分32
基于 RRAM 的径向基函数神经元硬件实现，用于边缘分类器
An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers
AI 导读
一篇论文提出用 Metal-Oxide RRAM 基模拟内容可寻址存储器（ACAM）作为边缘分类的硬件基底，核心是自研 Template piXeL（TXL）单元，每个单元作为可配置感受野神经元并用径向基激活函数计算输入与已编程感受野的距离。
正文
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.
| Subjects: | Emerging Technologies (cs.ET); Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2606.14739 [cs.ET] |
| (or arXiv:2606.14739v2 [cs.ET] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14739 arXiv-issued DOI via DataCite |
Submission history
From: Georgios Papandroulidakis Dr [view email]
[v1]
Tue, 2 Jun 2026 15:08:11 UTC (8,067 KB)
[v2]
Wed, 7 Oct 2026 09:38:38 UTC (8,439 KB)
来源：arXiv：cs.LG · arxiv.org