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 …
Energy-efficient deep learning inference on edge devices
The success of deep learning comes at the cost of very high computational complexity.
Consequently, Internet of Things (IoT) edge nodes typically offload deep learning tasks to …
Consequently, Internet of Things (IoT) edge nodes typically offload deep learning tasks to …
Efficient methods and hardware for deep learning
S Han - 2017 - search.proquest.com
The future will be populated with intelligent devices that require inexpensive, low-power
hardware platforms. Deep neural networks have evolved to be the state-of-the-art technique …
hardware platforms. Deep neural networks have evolved to be the state-of-the-art technique …
An overview of energy-efficient hardware accelerators for on-device deep-neural-network training
J Lee, HJ Yoo - IEEE Open Journal of the Solid-State Circuits …, 2021 - ieeexplore.ieee.org
Deep Neural Networks (DNNs) have been widely used in various artificial intelligence (AI)
applications due to their overwhelming performance. Furthermore, recently, several …
applications due to their overwhelming performance. Furthermore, recently, several …
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 …
Energy-aware AI-driven framework for edge-computing-based IoT applications
The significant growth in the number of Internet of Things (IoT) devices has given impetus to
the idea of edge computing for several applications. In addition, energy harvestable or …
the idea of edge computing for several applications. In addition, energy harvestable or …
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 …
Deep learning with edge computing: A review
Deep learning is currently widely used in a variety of applications, including computer vision
and natural language processing. End devices, such as smartphones and Internet-of-Things …
and natural language processing. End devices, such as smartphones and Internet-of-Things …
Deep learning for edge computing applications: A state-of-the-art survey
With the booming development of Internet-of-Things (IoT) and communication technologies
such as 5G, our future world is envisioned as an interconnected entity where billions of …
such as 5G, our future world is envisioned as an interconnected entity where billions of …
Moving deep learning to the edge
Deep learning is now present in a wide range of services and applications, replacing and
complementing other machine learning algorithms. Performing training and inference of …
complementing other machine learning algorithms. Performing training and inference of …