An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers
Researchers have developed a novel hardware implementation for a Radial Basis Function (RBF) neuron using Metal-Oxide Resistive RAM (RRAM) technology. This design, based on a custom Template piXeL (TXL) cell, acts as an efficient substrate for metric-based classification and online adaptation on resource-constrained edge devices. Simulations show the RRAM-based RBF classifier achieving 89.1% accuracy on the MNIST dataset with low energy consumption. AI
IMPACT This RRAM-based hardware could enable more efficient and lower-power AI inference on edge devices.