52 citations · 148 across the 19 of their papers we have counts for
20 papers
Multi-agent LLMs on the Cloud-Edge-IoT Continuum: A Systematic Mapping Study on Architectures, Deployment, and Evaluation
Mahyar Tourchi Moghaddam, Tiziano Santilli, Leo Wilhelm Lierse +2
LLM applications are increasingly built as systems of cooperating components rather than as single prompt-response pipelines. IoT applications add constraints of their own, because…
MAUPITI: On-Device Prototype-Based Learning on a Smart Infrared Sensor
Beatrice Alessandra Motetti, Tanguy Dugas du Villard, Matteo Risso +7
Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems. In this letter, we describe a smart multi-pix…
Persona-as-Configuration: Generative Stakeholder Reporting for Agricultural Floods
Oliver Aleksander Larsen, Tiziano Santilli, Francesco Daghero +1
Cyber-physical systems built on deterministic edge inference, such as on-vehicle flood detection for agricultural fields, produce structured decision logs that must be interpreted…
SAKE: Software Architectural Knowledge Evaluation Benchmark for Large Language Models
Tiziano Santilli, Francesco Daghero, Mayhar Tourchi Moghaddam
Large Language Models (LLMs) are increasingly used as assistants across the software development lifecycle, yet their ability to reason about software architecture remains largely…
Hierarchical adaptive control for real-time dynamic inference at the edge
Francesco Daghero, Mahyar Tourchi Moghaddam, Mikkel Baun Kjærgaard
Industrial systems increasingly depend on Machine Learning (ML), and operate on heterogeneous nodes that must satisfy tight latency, energy, and memory constraints. Dynamic ML mode…
Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers
Francesco Daghero, Daniele Jahier Pagliari, Francesco Conti +3
The acceleration of pruned Deep Neural Networks (DNNs) on edge devices such as Microcontrollers (MCUs) is a challenging task, given the tight area- and power-constraints of these d…
MATCH: Model-Aware TVM-based Compilation for Heterogeneous Edge Devices
Mohamed Amine Hamdi, Francesco Daghero, Giuseppe Maria Sarda +6
Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware…
Machine Learning-based feasibility estimation of digital blocks in BCD technology
Gabriele Faraone, Francesco Daghero, Eugenio Serianni +5
Analog-on-Top Mixed Signal (AMS) Integrated Circuit (IC) design is a time-consuming process predominantly carried out by hand. Within this flow, usually, some area is reserved by t…
Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devices
Francesco Daghero, Alessio Burrello, Massimo Poncino +2
Depthwise separable convolutions are a fundamental component in efficient Deep Neural Networks, as they reduce the number of parameters and operations compared to traditional convo…
Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones
Matteo Risso, Francesco Daghero, Beatrice Alessandra Motetti +4
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonethele…
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays
Matteo Risso, Chen Xie, Francesco Daghero +6
Low-resolution infrared (IR) array sensors enable people counting applications such as monitoring the occupancy of spaces and people flows while preserving privacy and minimizing e…
Model-Driven Dataset Generation for Data-Driven Battery SOH Models
Khaled Sidahmed Sidahmed Alamin, Francesco Daghero, Giovanni Pollo +5
Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems. Since there is no practical way to instantaneously measure it a…
Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes
Francesco Daghero, Alessio Burrello, Enrico Macii +3
With the increasing popularity of Internet of Things (IoT) devices, there is a growing need for energy-efficient Machine Learning (ML) models that can run on constrained edge nodes…
Efficient Deep Learning Models for Privacy-preserving People Counting on Low-resolution Infrared Arrays
Chen Xie, Francesco Daghero, Yukai Chen +6
Ultra-low-resolution Infrared (IR) array sensors offer a low-cost, energy-efficient, and privacy-preserving solution for people counting, with applications such as occupancy monito…
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks
Francesco Daghero, Alessio Burrello, Chen Xie +6
Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on embedded devices, from smartphones to ultra low-power sensors. Due to the high computati…
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs
Francesco Daghero, Daniele Jahier Pagliari, Massimo Poncino
Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classi…
Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers
Francesco Daghero, Alessio Burrello, Chen Xie +5
Random Forests (RFs) are widely used Machine Learning models in low-power embedded devices, due to their hardware friendly operation and high accuracy on practically relevant tasks…
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors
Francesco Daghero, Chen Xie, Daniele Jahier Pagliari +6
Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning…
Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs
Chen Xie, Francesco Daghero, Yukai Chen +6
Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitori…
Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidence
Francesco Daghero, Alessio Burrello, Daniele Jahier Pagliari +3
Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency,…