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Found 11 papers, 2 papers with code
Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring.
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays
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 energy consumption.
Model-Driven Dataset Generation for Data-Driven Battery SOH Models
Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems.
Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes
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
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 monitoring.
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks
With experiments on four datasets, and targeting an ultra-low-power RISC-V MCU, we show that (i) We are able to obtain a rich set of Pareto-optimal CNNs for HAR, spanning more than 1 order of magnitude in terms of memory, latency and energy consumption; (ii) Thanks to adaptive inference, we can derive >20 runtime operating modes starting from a single CNN, differing by up to 10% in classification scores and by more than 3x in inference complexity, with a limited memory overhead; (iii) on three of the four benchmarks, we outperform all previous deep learning methods, reducing the memory occupation by more than 100x.
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs
In this work, we bridge the gap between on-device HAR and DL thanks to a hierarchical architecture composed of a decision tree (DT) and a one dimensional Convolutional Neural Network (1D CNN).
Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers
The accuracy of a RF often increases with the number of internal weak learners (decision trees), but at the cost of a proportional increase in inference latency and energy consumption.
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors
In this work, we propose a novel implementation of HAR based on deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting low-power general purpose processors with a RISC-V instruction set.
Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs
In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN).
Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidence
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, and improve privacy.
