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All Journal Journal of Embedded Systems, Security and Intelligent Systems
Nur Azizah Eka Budiarti
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Computer Science & IT

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Optimizing Convolution Operation Using Winograd Minimal Filtering Transformation Dary Mochamad Rifqie; Muh. Ma’ruf Idris; Nur Azizah Eka Budiarti
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7655

Abstract

Convolutional Neural Networks (CNNs) have achieved significant success in the field of computer vision; however, their high computational complexity poses challenges for deployment in real-time applications. This study explores the application of Winograd-based convolution algorithms, specifically F (2,3) and F (4,3), as a means to accelerate CNN inference. Using the VGG-16 architecture as a benchmark, we evaluate the performance of these algorithms in terms of execution time and computational accuracy. Experimental results demonstrate that Winograd F (2,3) reduces runtime by an average of 59.62%, while Winograd F (4,3) achieves a 39.81% reduction compared to standard convolution. Accuracy is assessed using single-precision 32-bit floating-point arithmetic, with results showing that Winograd F (2,3) achieves the lowest maximum element error in six out of nine convolutional layers. These findings indicate that Winograd-based methods offer an efficient alternative to conventional CNN computations, particularly in performance-constrained environments.
Co-Authors Muh. Ma’ruf Idris