Proceedings of tinyML Research Symposium – 2024
MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems – Flavio Ponzina, Tajana Rosing [arxiv] [paper]
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms – Riccardo Miccini, Alessandro Cerioli, Clément Laroche, Tobias Piechowiak, Jens Sparsø, Luca Pezzarossa [arxiv] [paper]
Tiny Graph Neural Networks for Radio Resource Management – Ahmad Ghasemi, Hossein Pishro-Nik [arxiv] [paper]
Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity – Samitha Somathilaka, Adrian Ratwatte, Sasitharan Balasubramaniam, Mehmet Can Vuran, Witawas Srisa-an, Pietro Liò [arxiv] [paper]
Comparing Classic Machine Learning Techniques with Deep Learning for TinyML Human Activity Recognition – Bruno MONTANARI [arxiv] [paper]
Energy-Aware FPGA Implementation of Spiking Neural Network with LIF Neurons – Mozhgan Navardi [arxiv] [paper]
Boosting keyword spotting through on-device learnable user speech characteristics – Cristian Cioflan, Lukas Cavigelli, Luca Benini [arxiv] [paper]
Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection – Jared M. Ping, Ken J. Nixon [arxiv] [paper]
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute In-Memory Hardwar – Souvik Kundu, Anthony Sarah, Vinay Joshi, Om J Omer, Sreenivas Subramoney [arxiv] [paper]
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Hardware – Hasib-Al Rashid [arxiv] [paper]
SpokeN-100: A Cross-Lingual Benchmarking Dataset for the Classification of Spoken Numbers in Different Languages
– René Groh, Nina Goes, Andreas M. Kist [arxiv] [paper]
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces – Yejia Liu, Shijin Duan, Xiaolin Xu, Shaolei Ren [arxiv] [paper]