48 citations · 110 across the 15 of their papers we have counts for
17 papers
Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge
Luca Crupi, Lorenzo Lamberti, Alessandro Giusti +1
Traditional TinyML systems for edge devices achieve high accuracy by relying on fixed-depth models that require a constant number of multiply-accumulate (MAC) operations regardless…
Thinking Fast, Thinking Slow: Adaptive Multimodal Transformer-based Sensor Fusion for Depth Estimation on Ultra-low-power MCUs
Luca Crupi, Lorenzo Lamberti, Giovanni Badaracco +3
Artificial intelligence (AI)-based multimodal sensor fusion is a relevant topic gaining ever more traction across ultra-low-power (ULP) embedded and cyber-physical systems, as it i…
Improving Autonomous Nano-drones Performance via Automated End-to-End Optimization and Deployment of DNNs
Vlad Niculescu, Lorenzo Lamberti, Francesco Conti +2
The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion of convolutional neural networks (CNNs) are fueling the advent of autonomous driving n…
Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System
Lorenzo Lamberti, Manuele Rusci, Marco Fariselli +2
In this paper, we present the first (to the best of our knowledge) demonstration of a low-power MCU-based edge device for Automatic License Plate Recognition (ALPR). The design lev…
TinyDEVO: Deep Event-based Visual Odometry on Ultra-low-power Multi-core Microcontrollers
Alessandro Marchei, Lorenzo Lamberti, Daniele Palossi +1
A key task in embedded vision is visual odometry (VO), which estimates camera motion from visual sensors, and it is a core component in many embedded power-constrained systems, fro…
Tiny-DroNeRF: Tiny Neural Radiance Fields aboard Federated Learning-enabled Nano-drones
Ilenia Carboni, Elia Cereda, Lorenzo Lamberti +3
Sub-30g nano-sized aerial robots can leverage their agility and form factor to autonomously explore cluttered and narrow environments, like in industrial inspection and search and…
Circuits and Systems for Embodied AI: Exploring uJ Multi-Modal Perception for Nano-UAVs on the Kraken Shield
Viviane Potocnik, Alfio Di Mauro, Lorenzo Lamberti +4
Embodied artificial intelligence (AI) requires pushing complex multi-modal models to the extreme edge for time-constrained tasks such as autonomous navigation of robots and vehicle…
Distilling Tiny and Ultra-fast Deep Neural Networks for Autonomous Navigation on Nano-UAVs
Lorenzo Lamberti, Lorenzo Bellone, Luka Macan +4
Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-Things smart sensors to collect information in narrow spaces. This requires ultra-fast naviga…
Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones
Lorenzo Lamberti, Vlad Niculescu, Michał Barcis +4
Pocket-sized autonomous nano-drones can revolutionize many robotic use cases, such as visual inspection in narrow, constrained spaces, and ensure safer human-robot interaction due…
Combining Local and Global Perception for Autonomous Navigation on Nano-UAVs
Lorenzo Lamberti, Georg Rutishauser, Francesco Conti +1
A critical challenge in deploying unmanned aerial vehicles (UAVs) for autonomous tasks is their ability to navigate in an unknown environment. This paper introduces a novel vision-…
Multi-sensory Anti-collision Design for Autonomous Nano-swarm Exploration
Mahyar Pourjabar, Manuele Rusci, Luca Bompani +4
This work presents a multi-sensory anti-collision system design to achieve robust autonomous exploration capabilities for a swarm of 10 cm-side nano-drones operating on object dete…
A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing
Lorenzo Lamberti, Elia Cereda, Gabriele Abbate +7
Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills. The recent emergence of autonomous nano-sized drone…
Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones
Lorenzo Lamberti, Luca Bompani, Victor Javier Kartsch +3
Nano-sized drones, with palm-sized form factor, are gaining relevance in the Internet-of-Things ecosystem. Achieving a high degree of autonomy for complex multi-objective missions…
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge
Matteo Risso, Alessio Burrello, Francesco Conti +6
Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Obj…
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
Thorir Mar Ingolfsson, Mark Vero, Xiaying Wang +3
The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces. Thus, limiting the search to h…
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks
Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari +5
Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks. One key feature of TCNs is time-dilated convolution, whose optimization…
Regularity Results for an Optimal Design Problem with lower order terms
Luca Esposito, Lorenzo Lamberti
We study the regularity of the interface for optimal energy configurations of functionals involving bulk energies with an additional perimeter penalization of the interface. It is…