52 citations · 278 across the 22 of their papers we have counts for
15 papers · 1 filter
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
Luigi Capogrosso, Alessio Mascolini, Federico Girella +10
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. 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…
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference
Matteo Risso, Alessio Burrello, Giuseppe Maria Sarda +5
The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a di…
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…
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…
A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling
Khaled Sidahmed Sidahmed Alamin, Yukai Chen, Enrico Macii +2
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject…
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes
Matteo Risso, Alessio Burrello, Luca Benini +3
Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks. In particular, mixed-precis…
Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks
Matteo Risso, Alessio Burrello, Luca Benini +3
Neural Architecture Search (NAS) is increasingly popular to automatically explore the accuracy versus computational complexity trade-off of Deep Learning (DL) architectures. When t…
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…
C-NMT: A Collaborative Inference Framework for Neural Machine Translation
Yukai Chen, Roberta Chiaro, Enrico Macii +2
Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for ot…
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,…
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…
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference
Alessio Burrello, Alberto Dequino, Daniele Jahier Pagliari +5
Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis. We introduce an automated exploration approach and a library of optim…