Recent advances in convolutional neural network acceleration
TLDR
In this paper, the authors present a taxonomy of CNN acceleration methods in terms of three levels, i.e. structure level, algorithm level, and implementation level, for CNN architectures compression, algorithm optimization and hardware-based improvement.About:
This article is published in Neurocomputing.The article was published on 2019-01-05 and is currently open access. It has received 233 citations till now. The article focuses on the topics: Convolutional neural network.read more
Citations
More filters
Journal ArticleDOI
A survey of the recent architectures of deep convolutional neural networks
TL;DR: Deep Convolutional Neural Networks (CNNs) as mentioned in this paper are a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
Journal ArticleDOI
Pruning and quantization for deep neural network acceleration: A survey
TL;DR: A survey on two types of network compression: pruning and quantization is provided, which compare current techniques, analyze their strengths and weaknesses, provide guidance for compressing networks, and discuss possible future compression techniques.
Journal ArticleDOI
Artificial intelligence and machine learning in design of mechanical materials
TL;DR: The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.
Journal ArticleDOI
State of the Art in Defect Detection Based on Machine Vision
TL;DR: A detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms.
Journal ArticleDOI
A survey of convolutional neural networks on edge with reconfigurable computing
TL;DR: Reconfigurable computing is being considered for inference on edge due to its high performance and energy efficiency while keeping a high hardware flexibility that allows for the easy adaption of the target computing platform to the CNN model.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.