136 citations · 266 across the 18 of their papers we have counts for
5 papers · 1 filter
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems
Manuele Rusci, Tinne Tuytelaars
A personalized KeyWord Spotting (KWS) pipeline typically requires the training of a Deep Learning model on a large set of user-defined speech utterances, preventing fast customizat…
Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers
Davide Nadalini, Manuele Rusci, Luca Benini +1
Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units (MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural Network (DNN) models…
A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays
Leonardo Ravaglia, Manuele Rusci, Davide Nadalini +3
In the last few years, research and development on Deep Learning models and techniques for ultra-low-power devices in a word, TinyML has mainly focused on a train-then-deploy assum…
Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers
Manuele Rusci, Marco Fariselli, Alessandro Capotondi +1
The severe on-chip memory limitations are currently preventing the deployment of the most accurate Deep Neural Network (DNN) models on tiny MicroController Units (MCUs), even if le…
Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers
Manuele Rusci, Alessandro Capotondi, Luca Benini
This paper presents a novel end-to-end methodology for enabling the deployment of low-error deep networks on microcontrollers. To fit the memory and computational limitations of re…