A Faster Algorithm for Reducing the Computational Complexity of Convolutional Neural Networks

Zhao, Yulin; Wang, Donghui; Wang, Leiou; Liu, Peng

doi:10.3390/a11100159

Cited by 19 publications

(12 citation statements)

References 13 publications

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“…Winograd filtering reduces the number of multiplications at the cost of more additions. The filtering can be efficiently implemented with lookup tables and shifts in FPGA [49][50][51].…”

Section: Reduction Of Complexity Of Cnn Modelsmentioning

confidence: 99%

A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing

Véstias

2019

Algorithms

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The convolutional neural network (CNN) is one of the most used deep learning models for image detection and classification, due to its high accuracy when compared to other machine learning algorithms. CNNs achieve better results at the cost of higher computing and memory requirements. Inference of convolutional neural networks is therefore usually done in centralized high-performance platforms. However, many applications based on CNNs are migrating to edge devices near the source of data due to the unreliability of a transmission channel in exchanging data with a central server, the uncertainty about channel latency not tolerated by many applications, security and data privacy, etc. While advantageous, deep learning on edge is quite challenging because edge devices are usually limited in terms of performance, cost, and energy. 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. In this paper, we described the features of the most common CNNs, the capabilities of reconfigurable computing for running CNNs, the state-of-the-art of reconfigurable computing implementations proposed to run CNN models, as well as the trends and challenges for future edge reconfigurable platforms.

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“…Winograd filtering reduces the number of multiplications at the cost of more additions. The filtering can be efficiently implemented with lookup tables and shifts in FPGA [49][50][51].…”

Section: Reduction Of Complexity Of Cnn Modelsmentioning

confidence: 99%

A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing

Véstias

2019

Algorithms

View full text Add to dashboard Cite

show abstract

“…[29] and Zhao et al . [30], we have computed the computation complexity of our proposed two‐stage CNN. The complexity of the first stage is

904.5 \times 10^{8}

and the second stage is

1.15 \times 10^{8}

.…”

Section: Results and Disscussionmentioning

confidence: 99%

CNN based localisation of forged region in object‐based forgery for HD videos

Kohli

Gupta

Singhal

2020

IET Image Processing

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“…Winograd filtering is a known technique to reduce the number of multiplications of a convolution. The technique was efficiently implemented on FPGA [147][148][149][150].…”

Section: Hardware-oriented Deep Neural Network Optimizationsmentioning

confidence: 99%

Moving Deep Learning to the Edge

et al. 2020

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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 deep neural networks using the cloud computing model is not viable for applications where low latency is required. Furthermore, the rapid proliferation of the Internet of Things will generate a large volume of data to be processed, which will soon overload the capacity of cloud servers. One solution is to process the data at the edge devices themselves, in order to alleviate cloud server workloads and improve latency. However, edge devices are less powerful than cloud servers, and many are subject to energy constraints. Hence, new resource and energy-oriented deep learning models are required, as well as new computing platforms. This paper reviews the main research directions for edge computing deep learning algorithms.

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A Faster Algorithm for Reducing the Computational Complexity of Convolutional Neural Networks

Cited by 19 publications

References 13 publications

A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing

A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing

CNN based localisation of forged region in object‐based forgery for HD videos

Moving Deep Learning to the Edge

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