Lowering dynamic power of a stream-based CNN hardware accelerator

Lowering dynamic power of a stream-based CNN hardware accelerator

Custom hardware accelerators of Convolutional Neural Networks (CNN) provide a promising solution to meet real-time constraints for a wide range of applications on low-cost embedded devices. In this work, we aim to lower the dynamic power of a stream-based CNN hardware accelerator by reducing the com...

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Bibliographic Details
Main Authors: Piyasena, Duvindu, Wickramasinghe, Rukshan, Paul, Debdeep, Lam, Siew-Kei, Wu, Meiqing
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2021
Subjects:
Engineering::Computer science and engineering::Hardware::Register-transfer-level implementation
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
FPGA
Convolutional Neural Networks
Online Access:https://hdl.handle.net/10356/147509

Internet

https://hdl.handle.net/10356/147509

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