New Breakthrough in TinyML! Efficient Indoor Localization Using Transformers and Mamba
Paper Title: Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge DevicesPublication Date: December 2024Authors: Thanaphon Suwannaphong, Ferdian Jovan, Ian Craddock, Ryan McConvilleAffiliations: University of Bristol, University of AberdeenOriginal Link: https://arxiv.org/pdf/2412.09289Open Source Code and Dataset Link: https://github.com/AloeUoB/tinyML_indoor_localisation Introduction Typically, accurate indoor localization systems rely on large machine learning … Read more