Stride 2 1-D, 2-D, and 3-D Winograd for Convolutional Neural Networks

Yépez, Juan; Ko, Seok‐Bum

doi:10.1109/tvlsi.2019.2961602

Cited by 50 publications

(34 citation statements)

References 26 publications

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“…Two types of strides are used: Stride 1 (s1) and Stride 2 (s2). Stride represents the element-wise shift displacement of a kernel over an input along a particular axis [11]. Stride 1 will move one filter at a time, and Stride 2 will move two filters at a time.…”

Section: Related Work a Mobilenetmentioning

confidence: 99%

Light-weight MobileNet for Fast Detection of COVID-19

Firmansyah

Koh

Dewanto

et al. 2021

J-TIT

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The machine learning models based on Convolutional Neural Networks (CNNs) can be effectively used for detection and recognition of objects, such as Corona Virus Disease 19 (COVID-19). In particular, the MobileNet and Single Shot multi-box Detector (SSD) have recently been proposed as the machine learning model for object detection. However, there are still some challenges for deployment of such architectures on the embedded devices, due to the limited computational power. Another problem is that the accuracy of the associated machine learning model may be decreased, depending on the number of concerned parameters and layers. This paper proposes a light-weight MobileNet (LMN) architecture that can be used to improve the accuracy of the machine learning model, with a small number of layers and lower computation time, compared to the existing models. By experimentation, we show that the proposed LMN model can be effectively used for detection of COVID-19 virus. The proposed LMN can achieve the accuracy of 98% with the file size of 27.8 Mbits by replacing the standard CNN layers with separable convolutional layers.

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Section: Related Work a Mobilenetmentioning

confidence: 99%

Light-weight MobileNet for Fast Detection of COVID-19

Firmansyah

Koh

Dewanto

et al. 2021

J-TIT

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“…Such large transformation matrices result in complex pre-computation and more latency. Yang et al [32] and Yepez and Ko [33] further applied the Winograd algorithm for 2-stride convolution by decomposing the input feature map titles and kernels.…”

Section: Related Workmentioning

confidence: 99%

“…Refs. [32,33] proposed a strategy of decomposing 2-D 2-stride convolution, that is, decomposing and recombining the input feature title and convolution kernel according to the location of elements. Then, each decomposed feature sub-titles only do convolution with the corresponding decomposed sub-kernel.…”

Section: Winograd Algorithmmentioning

confidence: 99%

“…Various convolution accelerators based on the Winograd algorithm have been proposed [28][29][30][31]. Moreover, [32,33] proposed a method of optimizing 2-stride convolution with the Winograd algorithm, which reduces the design complexity of the Winograd-based CNN accelerator and significantly enhances the computation efficiency. However, the Winograd algorithm is not the best optimization scheme for the upsampling in U-Net because of the sparse feature map after expansion.…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

Cai

Wang

2021

Electronics

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Real-time object detection is a challenging but crucial task for autonomous underwater vehicles because of the complex underwater imaging environment. Resulted by suspended particles scattering and wavelength-dependent light attenuation, underwater images are always hazy and color-distorted. To overcome the difficulties caused by these problems to underwater object detection, an end-to-end CNN network combined U-Net and MobileNetV3-SSDLite is proposed. Furthermore, the FPGA implementation of various convolution in the proposed network is optimized based on the Winograd algorithm. An efficient upsampling engine is presented, and the FPGA implementation of squeeze-and-excitation module in MobileNetV3 is optimized. The accelerator is implemented on a Zynq XC7Z045 device running at 150 MHz and achieves 23.68 frames per second (fps) and 33.14 fps when using MobileNetV3-Large and MobileNetV3-Small as the feature extractor. Compared to CPU, our accelerator achieves 7.5×–8.7× speedup and 52×–60× energy efficiency.

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“…Down-sampling often uses a stride convolution operator to reduce the size of the feature map through non-unit step convolution. [19] extended the algorithm to three dimensions while achieving a step size of 2.…”

Section: Generalizationmentioning

confidence: 99%

Fast Convolution based on Winograd Minimum Filtering: Introduction and Development

Gan¹,

Huang²

2021

Computer Science and Information Technology Trends

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Convolutional Neural Network (CNN) has been widely used in various fields and played an important role. Convolution operators are the fundamental component of convolutional neural networks, and it is also the most time-consuming part of network training and inference. In recent years, researchers have proposed several fast convolution algorithms including FFT and Winograd. Among them, Winograd convolution significantly reduces the multiplication operations in convolution, and it also takes up less memory space than FFT convolution. Therefore, Winograd convolution has quickly become the first choice for fast convolution implementation within a few years. At present, there is no systematic summary of the convolution algorithm. This article aims to fill this gap and provide detailed references for follow-up researchers. This article summarizes the development of Winograd convolution from the three aspects of algorithm expansion, algorithm optimization, implementation, and application, and finally makes a simple outlook on the possible future directions.

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Stride 2 1-D, 2-D, and 3-D Winograd for Convolutional Neural Networks

Cited by 50 publications

References 26 publications

Light-weight MobileNet for Fast Detection of COVID-19

Light-weight MobileNet for Fast Detection of COVID-19

A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

Fast Convolution based on Winograd Minimum Filtering: Introduction and Development

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