High-speed operation, an FPGA-based convolutional neural network by pipelined accelerator

CNN models are widely utilized because they have shown outstanding results in various fields, including computer vision and speech recognition. This success has been aided by the widespread availability of capable underlying hardware platforms. Currently, hardware specialization may expose us to new...

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Bibliographic Details
Main Authors: Umapathy, K., Sivanantham, E., Epsiba, P., Gopi, B., Kumar, S. Mohan
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial neural networks
Computer vision
Hardware
Platforms
Speech recognition
Online Access:Get full text
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Summary:CNN models are widely utilized because they have shown outstanding results in various fields, including computer vision and speech recognition. This success has been aided by the widespread availability of capable underlying hardware platforms. Currently, hardware specialization may expose us to new architectural solutions that outperform general-purpose computers for the laser operations involved. Although different applications need different performance parameters, they place a high value on speed and energy efficiency. Meanwhile, processing has resurged as a result of its inherited high speed and high-power features. This paper looks at the potential of using CNNs with a suggested accelerator based on a modified filter algorithm. Our results show that pipeline accelerators may increase energy efficiency while competing with state-of-the-art electronic platforms in terms of power and speed. The proposed design is Xilinx ISE synthesizable and simulated.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0110693