Edge intelligence: Challenges and opportunities of near-sensor machine learning applications
The number of connected IoT devices is expected to reach over 20 billion by 2020. These
range from basic sensor nodes that log and report the data for cloud processing, to the ones …
range from basic sensor nodes that log and report the data for cloud processing, to the ones …
An overview of next-generation architectures for machine learning: Roadmap, opportunities and challenges in the IoT era
M Shafique, T Theocharides… - … , Automation & Test …, 2018 - ieeexplore.ieee.org
The number of connected Internet of Things (IoT) devices are expected to reach over 20
billion by 2020. These range from basic sensor nodes that log and report the data to the …
billion by 2020. These range from basic sensor nodes that log and report the data to the …
Flying iot: Toward low-power vision in the sky
The Internet of Things (IoT) is rapidly enabling applications in many different fields by
embedding itself into the physical world. Many potential IoT devices require some level of …
embedding itself into the physical world. Many potential IoT devices require some level of …
Deep learning at the edge
The ever-increasing number of Internet of Things (IoT) devices has created a new computing
paradigm, called edge computing, where most of the computations are performed at the …
paradigm, called edge computing, where most of the computations are performed at the …
DeepEdgeBench: Benchmarking deep neural networks on edge devices
EdgeAI (Edge computing based Artificial Intelligence) has been most actively researched for
the last few years to handle variety of massively distributed AI applications to meet up the …
the last few years to handle variety of massively distributed AI applications to meet up the …
Embedded deep learning for vehicular edge computing
J Hochstetler, R Padidela, Q Chen… - 2018 IEEE/ACM …, 2018 - ieeexplore.ieee.org
The accuracy of object recognition has been greatly improved due to the rapid development
of deep learning, but the deep learning generally requires a lot of training data and the …
of deep learning, but the deep learning generally requires a lot of training data and the …
Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices
M Verhelst, B Moons - IEEE Solid-State Circuits Magazine, 2017 - ieeexplore.ieee.org
Deep learning has recently become immensely popular for image recognition, as well as for
other recognition and pattern matching tasks in, eg, speech processing, natural language …
other recognition and pattern matching tasks in, eg, speech processing, natural language …
Bandwidth-efficient live video analytics for drones via edge computing
Real-time video analytics on small autonomous drones poses several difficult challenges at
the intersection of wireless bandwidth, processing capacity, energy consumption, result …
the intersection of wireless bandwidth, processing capacity, energy consumption, result …
In-situ ai: Towards autonomous and incremental deep learning for iot systems
Recent years have seen an exploration of data volumes from a myriad of IoT devices, such
as various sensors and ubiquitous cameras. The deluge of IoT data creates enormous …
as various sensors and ubiquitous cameras. The deluge of IoT data creates enormous …
A survey on deep learning empowered IoT applications
The Internet of Things (IoT) is widely regarded as a key component of the Internet of the
future and thereby has drawn significant interests in recent years. IoT consists of billions of …
future and thereby has drawn significant interests in recent years. IoT consists of billions of …