145,600 research outputs found
Compact optimized deep learning model for edge: a review
Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weighted CNN models on resource-constraint embedded systems and limited edge computing resources, such as memory, and battery constraints, poses significant challenges in developing the compact optimized model. Reducing the energy consumption in edge IoT networks is possible by reducing the computation and data transmission between IoT devices and gateway devices. Hence there is a high demand for making energy-efficient deep learning models for deploying on edge devices. Furthermore, recent studies show that smaller compressed models achieve significant performance compared to larger deep-learning models. This review article focuses on state-of-the-art techniques of edge intelligence, and we propose a new research framework for designing a compact optimized deep learning (DL) model deployment on edge devices
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. Previous work has shown that Deep
Learning (DL) can yield superior performance on this task. However, the
literature was missing an extensive comparative analysis of various efficient
DL architectures for IR array-based people counting, that considers not only
their accuracy, but also the cost of deploying them on memory- and
energy-constrained Internet of Things (IoT) edge nodes. In this work, we
address this need by comparing 6 different DL architectures on a novel dataset
composed of IR images collected from a commercial 8x8 array, which we made
openly available. With a wide architectural exploration of each model type, we
obtain a rich set of Pareto-optimal solutions, spanning cross-validated
balanced accuracy scores in the 55.70-82.70% range. When deployed on a
commercial Microcontroller (MCU) by STMicroelectronics, the STM32L4A6ZG, these
models occupy 0.41-9.28kB of memory, and require 1.10-7.74ms per inference,
while consuming 17.18-120.43 J of energy. Our models are significantly
more accurate than a previous deterministic method (up to +39.9%), while being
up to 3.53x faster and more energy efficient. Further, our models' accuracy is
comparable to state-of-the-art DL solutions on similar resolution sensors,
despite a much lower complexity. All our models enable continuous, real-time
inference on a MCU-based IoT node, with years of autonomous operation without
battery recharging.Comment: This article has been accepted for publication in IEEE Internet of
Things Journa
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks
Federated learning (FL) is a distributed and privacy-preserving learning
framework for predictive modeling with massive data generated at the edge by
Internet of Things (IoT) devices. One major challenge preventing the wide
adoption of FL in IoT is the pervasive power supply constraints of IoT devices
due to the intensive energy consumption of battery-powered clients for local
training and model updates. Low battery levels of clients eventually lead to
their early dropouts from edge networks, loss of training data jeopardizing the
performance of FL, and their availability to perform other designated tasks. In
this paper, we propose FedLE, an energy-efficient client selection framework
that enables lifespan extension of edge IoT networks. In FedLE, the clients
first run for a minimum epoch to generate their local model update. The models
are partially uploaded to the server for calculating similarities between each
pair of clients. Clustering is performed against these client pairs to identify
those with similar model distributions. In each round, low-powered clients have
a lower probability of being selected, delaying the draining of their
batteries. Empirical studies show that FedLE outperforms baselines on benchmark
datasets and lasts more training rounds than FedAvg with battery power
constraints.Comment: 6 pages, 6 figures, accepted to 2023 IEEE International Conference on
Communications (ICC 2023
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