Results 21 to 30 of about 2,687,799 (289)
Mode Switching for Secure Edge Devices
, 2022 Many devices in various domains operate in different modes. We have suggested to use mode switching for security purposes to make systems more resilient when vulnerabilities are known or when attacks are performed. We will demonstrate the usefulness of mode switching in the context of industrial edge devices.
Riegler, Michael +2 more
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Arithmetic Coding-Based 5-Bit Weight Encoding and Hardware Decoder for CNN Inference in Edge Devices
IEEE Access, 2021 Convolutional neural networks (CNNs) have gained a huge attention for real-world artificial intelligence (AI) applications such as image classification and object detection. On the other hand, for better accuracy, the size of the CNNs’ parameters (
Jong Hun Lee, Joonho Kong, Arslan Munir
doaj +1 more source
Proceedings of the 2019 Great Lakes Symposium on VLSI, 2019 The increasing ubiquity of edge devices in the consumer market, along with their ever more computationally expensive workloads, necessitate corresponding increases in computing power to support such workloads. In-memory computing is attractive in edge devices as it reuses preexisting memory elements, thus limiting area overhead.
Simon, William Andrew +4 more
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Edge Devices Inference Performance Comparison
Journal of Computing Science and Engineering, 2023 In this work, we investigate the inference time of the MobileNet family, EfficientNet V1 and V2 family, VGG models, Resnet family, and InceptionV3 on four edge platforms. Specifically NVIDIA Jetson Nano, Intel Neural Stick, Google Coral USB Dongle, and Google Coral PCIe.
Rafal Tobiasz +4 more
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Efficient Image Captioning for Edge Devices
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Recent years have witnessed the rapid progress of image captioning. However, the demands for large memory storage and heavy computational burden prevent these captioning models from being deployed on mobile devices. The main obstacles lie in the heavyweight visual feature extractors (i.e., object detectors) and complicated cross-modal fusion networks ...
Ning Wang +6 more
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Idleness-Aware Dynamic Power Mode Selection on the i.MX 7ULP IoT Edge Processor
Journal of Low Power Electronics and Applications, 2020 Power management is a crucial concern in micro-controller platforms for the Internet of Things (IoT) edge. Many applications present a variable and difficult to predict workload profile, usually driven by external inputs.
Alfio Di Mauro +3 more
doaj +1 more source
Measurement of MAST edge ion temperatures and velocities [PDF]
, 2011 A novel experimental method using gas puffing of neutrals to stimulate charge exchange emission has been developed to measure ion temperatures and toroidal rotation velocities in the edge of MAST.
Morgan, Thomas
core +5 more sources
Enabling Deep Learning on Edge Devices
CoRR, 2022 Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision, natural language processing, reinforcement learning, etc. The high-performed DNNs heavily rely on intensive resource consumption. For example, training a DNN requires high dynamic memory, a large-scale dataset, and a large number of computations (a ...
openaire +3 more sources
Digital Communications and NetworksEdge technology aims to bring cloud resources (specifically, the computation, storage, and network) to the closed proximity of the edge devices, i.e., smart devices where the data are produced and consumed.
Sabuzima Nayak +3 more
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Improving Far-Edge Device Management in IoT Applications Using Kubernetes
IEEE Open Journal of the Industrial Electronics SocietyInternet of Things (IoT) driven digitalization is shifting data processing to the edge, reducing the burden of constant cloud communication. Advances in resource-constrained microcontroller-based IoT devices that interact with the environment, such as in
Carlos Resende +4 more
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