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20172026
most citedPULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors

136 citations · 266 across the 18 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

eess.IV2024

Accelerating Image-based Pest Detection on a Heterogeneous Multi-core Microcontroller

Luca Bompani, Luca Crupi, Daniele Palossi +5

The codling moth pest poses a significant threat to global crop production, with potential losses of up to 80% in apple orchards. Special camera-based sensor nodes are deployed in…

cs.SD2024★ 5 cited

Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors

Manuele Rusci, Francesco Paci, Marco Fariselli +2

This paper proposes a self-learning method to incrementally train (fine-tune) a personalized Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio sensor…

cs.CV2024

Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems

Luca Bompani, Manuele Rusci, Daniele Palossi +2

This paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack), a novel video object detection framework for ultra-low-power embedded processors. This method reduces th…

cs.SD2024

On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems

Cristian Cioflan, Lukas Cavigelli, Manuele Rusci +2

Keyword spotting accuracy degrades when neural networks are exposed to noisy environments. On-site adaptation to previously unseen noise is crucial to recovering accuracy loss, and…

cs.RO2024

On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors

Elia Cereda, Manuele Rusci, Alessandro Giusti +1

Sub-\SI{50}{\gram} nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite se…