[HTML][HTML] An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks
Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial
Intelligence (AI) applications. Their ability to go beyond human precision has made these …
Intelligence (AI) applications. Their ability to go beyond human precision has made these …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Efficient hardware architectures for accelerating deep neural networks: Survey
In the modern-day era of technology, a paradigm shift has been witnessed in the areas
involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep …
involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep …
Review of ASIC accelerators for deep neural network
Deep neural networks (DNNs) have become an essential tool in artificial intelligence, with a
wide range of applications such as computer vision, medical diagnosis, security, robotics …
wide range of applications such as computer vision, medical diagnosis, security, robotics …
The hardware and algorithm co-design for energy-efficient DNN processor on edge/mobile devices
Deep neural network (DNN) has been widely studied due to its high performance and
usability for various applications such as image classification, detection, segmentation …
usability for various applications such as image classification, detection, segmentation …
Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices
A recent trend in deep neural network (DNN) development is to extend the reach of deep
learning applications to platforms that are more resource and energy-constrained, eg …
learning applications to platforms that are more resource and energy-constrained, eg …
A survey on convolutional neural network accelerators: GPU, FPGA and ASIC
Y Hu, Y Liu, Z Liu - 2022 14th International Conference on …, 2022 - ieeexplore.ieee.org
In recent years, artificial intelligence (AI) has been under rapid development, applied in
various areas. Among a vast number of neural network (NN) models, the convolutional …
various areas. Among a vast number of neural network (NN) models, the convolutional …
Ascend: a scalable and unified architecture for ubiquitous deep neural network computing: Industry track paper
H Liao, J Tu, J Xia, H Liu, X Zhou… - … Symposium on High …, 2021 - ieeexplore.ieee.org
Deep neural networks (DNNs) have been successfully applied to a great variety of
applications, ranging from small IoT devices to large scale services in a data center. In order …
applications, ranging from small IoT devices to large scale services in a data center. In order …
A survey on the optimization of neural network accelerators for micro-ai on-device inference
Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …
[图书][B] Efficient processing of deep neural networks
This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …