5 papers
PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors
Nathan Duboisset, Zhaolan Huang, Felix Bießmann +3
Recent progress in the field of TinyML has demonstrated that low-power hardware based on microcontrollers can achieve bird species monitoring in real time based on acoustic sensor…
Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollers
Zhaolan Huang, Kaspar Schleiser, Gyungmin Myung +1
Low-power microcontroller (MCU) hardware is currently evolving from single-core architectures to predominantly multi-core architectures. In parallel, new embedded software building…
TinyDéjàVu: Smaller RAM and Faster Inference with Neural Networks on MCUs for Sensor Data Streams
Zhaolan Huang, Emmanuel Baccelli
Examples of embedded intelligence include a wide variety of tiny neural networks used on-board wireless sensors and actuators, which are expected to continuously perform inference…
msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML
Zhaolan Huang, Emmanuel Baccelli
AI spans from large language models to tiny models running on microcontrollers (MCUs). Extremely memory-efficient model architectures are decisive to fit within an MCU's tiny memor…
TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
Zhaolan Huang, Adrien Tousnakhoff, Polina Kozyr +5
Monitoring biodiversity at scale is challenging. Detecting and identifying species in fine grained taxonomies requires highly accurate machine learning (ML) methods. Training such…