Unveiling energy efficiency in deep learning: Measurement, prediction, and scoring across edge devices
Today, deep learning optimization is primarily driven by research focused on achieving high
inference accuracy and reducing latency. However, the energy efficiency aspect is often …
inference accuracy and reducing latency. However, the energy efficiency aspect is often …
Design space exploration of a sparse mobilenetv2 using high-level synthesis and sparse matrix techniques on FPGAs
Convolution Neural Networks (CNNs) are gaining ground in deep learning and Artificial
Intelligence (AI) domains, and they can benefit from rapid prototyping in order to produce …
Intelligence (AI) domains, and they can benefit from rapid prototyping in order to produce …
An empirical study on the performance and energy consumption of ai containerization strategies for computer-vision tasks on the edge
RM Hampau, M Kaptein, R Van Emden, T Rost… - Proceedings of the 26th …, 2022 - dl.acm.org
Context. The rise of use cases of AI catered towards the Edge, where devices have limited
computation power and storage capabilities, motivates the need for better understating of …
computation power and storage capabilities, motivates the need for better understating of …
Web pages from mockup design based on convolutional neural network and class activation mapping
AAJ Cizotto, RCT de Souza, VC Mariani… - Multimedia Tools and …, 2023 - Springer
The objective of this study is to validate the use of Deep Neural Networks (DNNs) to
segment and classify web elements. To achieve this, a dataset of 2200 images was created …
segment and classify web elements. To achieve this, a dataset of 2200 images was created …
A reconfigurable neural architecture for edge–cloud collaborative real-time object detection
Although recent advances in deep neural networks (DNNs) have enabled remarkable
performance on various computer vision tasks, it is challenging for edge devices to perform …
performance on various computer vision tasks, it is challenging for edge devices to perform …
Hpc ai500 v2. 0: The methodology, tools, and metrics for benchmarking hpc ai systems
Recent years witness a trend of applying large-scale distributed deep learning algorithms
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
Beyond the model: Data pre-processing attack to deep learning models in Android apps
The increasing popularity of deep learning (DL) models and the advantages of computing,
including low latency and bandwidth savings on smartphones, have led to the emergence of …
including low latency and bandwidth savings on smartphones, have led to the emergence of …
Data-Driven Onboard Inter-Turn Short Circuit Fault Diagnosis for Electric Vehicles by Using Real-Time Simulation Environment
Á Zsuga, A Dineva - IEEE Access, 2023 - ieeexplore.ieee.org
Various fault detection methods, particularly focused on onboard Condition-Based
Monitoring (CBM) in Electrical Machines and Drives (EMDs), face limitations such as …
Monitoring (CBM) in Electrical Machines and Drives (EMDs), face limitations such as …
Building lightweight deep learning models with TensorFlow Lite for human activity recognition on mobile devices
SÖ Bursa, Ö Durmaz İncel, G Işıklar Alptekin - Annals of …, 2023 - Springer
Human activity recognition (HAR) is a research domain that enables continuous monitoring
of human behaviors for various purposes, from assisted living to surveillance in smart home …
of human behaviors for various purposes, from assisted living to surveillance in smart home …
Latency estimation tool and investigation of neural networks inference on mobile gpu
A lot of deep learning applications are desired to be run on mobile devices. Both accuracy
and inference time are meaningful for a lot of them. While the number of FLOPs is usually …
and inference time are meaningful for a lot of them. While the number of FLOPs is usually …