Fast and Scalable 2D Convolutions and Cross-correlations for Processing Image Databases and Videos on CPUs

The dominant use of Convolutional Neural Networks (CNNs) in several image and video analysis tasks necessitates a careful re-evaluation of the underlying software libraries for computing them for large-scale image and video databases. We focus our attention on developing methods that can be applied...

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Vydáno v:Proceedings (IEEE Southwest Symposium on Image Analysis and Interpretation) s. 70 - 73
Hlavní autoři: Carranza, Cesar, Llamocca, Daniel, Pattichis, Marios
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.03.2020
Témata:
Convolution
CPU
Image Processing
Kernel
Libraries
Memory management
MIMD
Optimization
Parallel Processing
SIMD
Throughput
Two dimensional displays
Videos
ISSN:2473-3598
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Shrnutí:The dominant use of Convolutional Neural Networks (CNNs) in several image and video analysis tasks necessitates a careful re-evaluation of the underlying software libraries for computing them for large-scale image and video databases. We focus our attention on developing methods that can be applied to large image databases or videos of large image sizes.We develop a method that maximizes throughput through the use of vector-based memory I/O and optimized 2D FFT libraries that run on all available physical cores. We also show how to decompose arbitrarily large images into smaller, optimal blocks that can be effectively processed through the use of overlap-and- add. Our approach outperforms Tensorflow for 5 × 5 kernels and significantly outperforms Tensorflow for 11 × 11 kernels.
ISSN:2473-3598
DOI:10.1109/SSIAI49293.2020.9094602