XiNet: Efficient Neural Networks for tinyML

no code implementations • ICCV 2023 • Alberto Ancilotto, Francesco Paissan, Elisabetta Farella

The recent interest in the edge-to-cloud continuum paradigm has emphasized the need for simple and scalable architectures to deliver optimal performance on computationally constrained devices.

Image Classification object-detection +1

Low-complexity acoustic scene classification in DCASE 2022 Challenge

no code implementations • 8 Jun 2022 • Irene Martín-Morató, Francesco Paissan, Alberto Ancilotto, Toni Heittola, Annamaria Mesaros, Elisabetta Farella, Alessio Brutti, Tuomas Virtanen

The provided baseline system is a convolutional neural network which employs post-training quantization of parameters, resulting in 46. 5 K parameters, and 29. 23 million multiply-and-accumulate operations (MMACs).

Acoustic Scene Classification Classification +2

PhiNets: a scalable backbone for low-power AI at the edge

no code implementations • 1 Oct 2021 • Francesco Paissan, Alberto Ancilotto, Elisabetta Farella

In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has become a necessity.