Please use this identifier to cite or link to this item:
Title: Lowering dynamic power of a stream-based CNN hardware accelerator
Authors: Piyasena, Duvindu
Wickramasinghe, Rukshan
Paul, Debdeep
Lam, Siew-Kei
Wu, Meiqing
Keywords: Engineering::Computer science and engineering::Hardware::Register-transfer-level implementation
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2019
Source: Piyasena, D., Wickramasinghe, R., Paul, D., Lam, S. & Wu, M. (2019). Lowering dynamic power of a stream-based CNN hardware accelerator. 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), 1-6.
Project: TUM CREATE 
Conference: 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)
Abstract: 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 computational redundancies in the CNN layers. In particular, we investigate the redundancies due to the downsampling effect of max pooling layers which are prevalent in state-of-the-art CNNs, and propose an approximation method to reduce the overall computations. The experimental results show that the proposed method leads to lower dynamic power without sacrificing accuracy.
ISBN: 9781728118178
DOI: 10.1109/MMSP.2019.8901777
Schools: School of Computer Science and Engineering 
Research Centres: Hardware & Embedded Systems Lab (HESL) 
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

Files in This Item:
File Description SizeFormat 
2019_mmsp_Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator.pdf266.51 kBAdobe PDFThumbnail

Citations 50

Updated on Jun 16, 2024

Page view(s)

Updated on Jun 22, 2024

Download(s) 50

Updated on Jun 22, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.