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Found 8 papers, 1 papers with code
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.
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.
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 and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference
Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces.
Enhanced search sensitivity to the double beta decay of $^{136}$Xe to excited states with topological signatures
Similarly, the half-life sensitivity of neutrinoless double beta decay of $^{136}$Xe to excited states of $^{136}$Ba can be improved by a factor of 4. 8 with topological signatures.
Nuclear Experiment High Energy Physics - Experiment
