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Found 28 papers, 11 papers with code
Foundation Models for Structural Health Monitoring
For AD, we achieve a near-perfect 99. 9% accuracy with a monitoring time span of just 15 windows.
Integrating SystemC-AMS Power Modeling with a RISC-V ISS for Virtual Prototyping of Battery-operated Embedded Devices
The combination of GVSoC and SystemC-AMS in a single simulation framework allows to perform a DSE that is dependent on the mutual impact between functional and extra-functional aspects.
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.
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices
The rapid proliferation of computing domains relying on Internet of Things (IoT) devices has created a pressing need for efficient and accurate deep-learning (DL) models that can run on low-power devices.
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
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.
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.
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference
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 diverse set of hardware accelerators.
Energy-efficient Wearable-to-Mobile Offload of ML Inference for PPG-based Heart-Rate Estimation
In this work, we propose a collaborative inference approach that uses both a smartwatch and a connected smartphone to maximize the performance of heart rate (HR) tracking while also maximizing the smartwatch's battery life.
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.
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge
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 Object Detection.
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.
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes
Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks.
A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling
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 to aging and performance deterioration over time.
Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks
When deployed on a commercial edge device, the STM NUCLEO-H743ZI2, our networks span a range of 2. 18x in energy consumption and 4. 04% in accuracy for the same memory constraint, and reduce energy by up to 2. 2x with negligible accuracy drop with respect to the baseline.
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).
C-NMT: A Collaborative Inference Framework for Neural Machine Translation
Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices.
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.
Robust and Energy-efficient PPG-based Heart-Rate Monitoring
A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU to enable non-invasive and comfortable monitoring, but ensuring robust PPG-based heart-rate monitoring in the presence of motion artifacts is still an open challenge.
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks
Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks.
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference
Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis.
Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices
Our most accurate quantized network achieves 4. 41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47. 65 mJ and a memory footprint of 412 kB.
Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition
Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms.
Adaptive Test-Time Augmentation for Low-Power CPU
Convolutional Neural Networks (ConvNets) are trained offline using the few available data and may therefore suffer from substantial accuracy loss when ported on the field, where unseen input patterns received under unpredictable external conditions can mislead the model.
W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality
To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training, and a Wiped module takes care of the final classification while broadcasting information on the prediction confidence at inference time.
