Year: 2025
Type: article
Abstract: Upscaling layers are important components of modern deep learning networks but often pose computational challenges for hardware accelerators. This paper addresses this issue by introducing a novel layer-replacement technique to efficiently process upscaling layers using existing hardware-supported operations like depthwise convolutions and maximum pooling. To minimize the number of replacement layers, we propose an efficient layer number reduction algorithm. Experimental results on 4 deep neural networks demonstrate a significant speedup ranging from 1.58x to 32.88x compared to the original HW/SW execution approach, and from 3.65x to 19.21x compared to the softwareonly solution, with minimal hardware overhead (0.068% more FPGA LUTs). Notably, our technique introduces no numerical errors and maintains comparable input data processing quality to the original network.
Institution: University of Novi Sad
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,645
OpenAlex ID: W4409723679