1. Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing: Detection and Classification
- Author
-
Kausar, Rizwana, Zayer, Fakhreddine, Viegas, Jaime, and Dias, Jorge
- Subjects
Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Robotics - Abstract
Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical tasks such as odor mapping, localization, and alarm generation. This paper introduces a hybrid approach that exploits neuromorphic computing in combination with probabilistic inference to address these demanding requirements. Our approach implements a combination of a convolutional spiking neural network for feature extraction and a Bayesian spiking neural network for odor detection and identification. The developed algorithm is rigorously tested on a dataset for sensor drift compensation for robustness evaluation. Additionally, for efficiency evaluation, we compare the energy consumption of our model with a non-spiking machine learning algorithm under identical dataset and operating conditions. Our approach demonstrates superior efficiency alongside comparable accuracy outcomes., Comment: 7 Pages, Conference
- Published
- 2024