A survey on edge performance benchmarking
Edge computing is the next Internet frontier that will leverage computing resources located
near users, sensors, and data stores to provide more responsive services. Therefore, it is …
near users, sensors, and data stores to provide more responsive services. Therefore, it is …
Automated pest detection with DNN on the edge for precision agriculture
A Albanese, M Nardello… - IEEE Journal on Emerging …, 2021 - ieeexplore.ieee.org
Artificial intelligence has smoothly penetrated several economic activities, especially
monitoring and control applications, including the agriculture sector. However, research …
monitoring and control applications, including the agriculture sector. However, research …
Autonomous, onboard vision-based trash and litter detection in low altitude aerial images collected by an unmanned aerial vehicle
Public littering and discarded trash are, despite the effort being put to limit it, still a serious
ecological, aesthetic, and social problem. The problematic waste is usually localised and …
ecological, aesthetic, and social problem. The problematic waste is usually localised and …
Faststereonet: A fast neural architecture search for improving the inference of disparity estimation on resource-limited platforms
Convolutional neural networks (CNNs) provide the best accuracy for disparity estimation.
However, CNNs are computationally expensive, making them unfavorable for resource …
However, CNNs are computationally expensive, making them unfavorable for resource …
Improving autonomous nano-drones performance via automated end-to-end optimization and deployment of dnns
The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion
of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano …
of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano …
An Optimized Multi-Task Learning Model for Disaster Classification and Victim Detection in Federated Learning Environments
Disaster classification and victim detection are two important tasks in enabling efficient
rescue operations. In this paper, we propose a multi-task learning (MTL) model which …
rescue operations. In this paper, we propose a multi-task learning (MTL) model which …
On-board crowd counting and density estimation using low altitude unmanned aerial vehicles—looking beyond beating the benchmark
Recent advances in deep learning-based image processing have enabled significant
improvements in multiple computer vision fields, with crowd counting being no exception …
improvements in multiple computer vision fields, with crowd counting being no exception …
Evaluation of deep learning accelerators for object detection at the edge
P Puchtler, R Peinl - KI 2020: Advances in Artificial Intelligence: 43rd …, 2020 - Springer
Deep learning is moving more and more from the cloud towards the edge. Therefore,
embedded devices are needed that are reasonably cheap, energy-efficient and fast enough …
embedded devices are needed that are reasonably cheap, energy-efficient and fast enough …
Reaching for the sky: Maximizing deep learning inference throughput on edge devices with ai multi-tenancy
The wide adoption of smart devices and Internet-of-Things (IoT) sensors has led to massive
growth in data generation at the edge of the Internet over the past decade. Intelligent real …
growth in data generation at the edge of the Internet over the past decade. Intelligent real …
AI multi-tenancy on edge: Concurrent deep learning model executions and dynamic model placements on edge devices
Many real-world applications are widely adopting the edge computing paradigm due to its
low latency and better privacy protection. With notable success in AI and deep learning (DL) …
low latency and better privacy protection. With notable success in AI and deep learning (DL) …