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 …
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 …
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 …
Edge machine learning for ai-enabled iot devices: A review
In a few years, the world will be populated by billions of connected devices that will be
placed in our homes, cities, vehicles, and industries. Devices with limited resources will …
placed in our homes, cities, vehicles, and industries. Devices with limited resources will …
Recent advances in evolving computing paradigms: Cloud, edge, and fog technologies
Cloud computing has become integral lately due to the ever-expanding Internet-of-things
(IoT) network. It still is and continues to be the best practice for implementing complex …
(IoT) network. It still is and continues to be the best practice for implementing complex …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …
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 …
Deep learning for edge computing: Current trends, cross-layer optimizations, and open research challenges
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …
their unmatchable performance in several applications, such as image processing, computer …
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 …
Secure edge computing in IoT systems: Review and case studies
M Alrowaily, Z Lu - 2018 IEEE/ACM symposium on edge …, 2018 - ieeexplore.ieee.org
Today, the architectures for efficient and secure network system designs, such as Internet of
Things (IoT) and big data analytics, are growing at a faster pace than ever before. Edge …
Things (IoT) and big data analytics, are growing at a faster pace than ever before. Edge …