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 …
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 …
How to evaluate deep neural network processors: Tops/w (alone) considered harmful
A significant amount of specialized hardware has been developed for processing deep
neural networks (DNNs) in both academia and industry. This article aims to highlight the key …
neural networks (DNNs) in both academia and industry. This article aims to highlight the key …
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 …
Embedded deep learning
B Moons, D Bankman, M Verhelst - Embedded Deep Learning, 2019 - Springer
Although state of the art in many typical machine learning tasks, deep learning algorithms
are very costly in terms of energy consumption, due to their large amount of required …
are very costly in terms of energy consumption, due to their large amount of required …
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 …
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 …
Efficient processing of deep neural networks: A tutorial and survey
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …
applications including computer vision, speech recognition, and robotics. While DNNs …
[PDF][PDF] Efficient processing of deep neural networks: a tutorial and survey
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …
applications including computer vision, speech recognition, and robotics. While DNNs …
Custom hardware architectures for deep learning on portable devices: a review
The staggering innovations and emergence of numerous deep learning (DL) applications
have forced researchers to reconsider hardware architecture to accommodate fast and …
have forced researchers to reconsider hardware architecture to accommodate fast and …