An Efficient Convolution Engine based on the À-trous Spatial Pyramid Pooling

Sestito, Cristian; Spagnolo, Fanny; Corsonello, Pasquale; Perri, Stefania

doi:10.1109/asap49362.2020.00022

Cited by 2 publications

(1 citation statement)

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“…They part the data by blocks and cut unnecessary parts off so they achieve faster results. Sestito et al (2020) offer a new method of convolution by applying a sliding window FIFO structure where multiple convolution layers work in parallel. They state that this module that they offer works approximately 650 times faster with 70 times less memory usage and 13 times less energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Classification on Accelerated Neural Networks

Sikdokur¹,

Baytas²,

Yurdakul³

2022

Preprint

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For image classification problems, various neural network models are commonly used due to their success in yielding high accuracies. Convolutional Neural Network (CNN) is one of the most frequently used deep learning methods for image classification applications. It may produce extraordinarily accurate results with regard to its complexity. However, the more complex the model is the longer it takes to train. In this paper, an acceleration design that uses the power of FPGA is given for a basic CNN model which consists of one convolutional layer and one fully connected layer for the training phase of the fully connected layer. Nonetheless, inference phase is also accelerated automatically due to the fact that training phase includes inference. In this design, the convolutional layer is calculated by the host computer and the fully connected layer is calculated by an FPGA board. It should be noted that the training of convolutional layer is not taken into account in this design and is left for future research. The results are quite encouraging as this FPGA design tops the performance of some of the state-ofthe-art deep learning platforms such as Tensorflow on the host computer approximately 2 times in both training and inference.

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Section: Literature Reviewmentioning

confidence: 99%