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Journal of Sensors
Volume 2016, Article ID 7281031, 9 pages
Research Article

A Tile-Based EGPU with a Fused Universal Processing Engine and Graphics Coprocessor Cluster

1School of Information Science and Engineering, Shandong University, No. 27, South Shanda Road, Jinan 250100, China
2Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, No. 336, West Nan Xinzhuang Road, Jinan 250022, China
3Administration Center, Shandong Academy of Information and Communication Technology, Jinan 250101, China

Received 16 March 2015; Revised 15 May 2015; Accepted 18 May 2015

Academic Editor: Gwanggil Jeon

Copyright © 2016 Yang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As various applied sensors have been integrated into embedded devices, the Embedded Graphics Processing Unit (EGPU) has assumed more processing tasks, which requires an EGPU with higher performance. A tile-based EGPU is proposed that can be used in both general-purpose computing and 3D graphics rendering. With fused, scalable, and hierarchical parallelism architecture, the EGPU has the ability to address nearly 100 million vertices or fragments and achieves 1 GFLOPS per second at a clock frequency of 200 MHz. A fused and scalable architecture, constituted by Universal Processing Engine (UPE) and Graphics Coprocessor Cluster (GCC), ensures that the EGPU can adapt to various graphic processing scenes and situations, achieving more efficient rendering. Moreover, hierarchical parallelism is implemented via the UPE. Additionally, tiling brings a significant reduction in both system memory bandwidth and power consumption. A 0.18 µm technology library is used for timing and power analysis. The area of the proposed EGPU is 6.5 mm 6.5 mm, and its power consumption is approximately 349.318 mW. Experimental results demonstrate that the proposed EGPU can be used in a System on Chip (SoC) configuration connected to sensors to accelerate its processing and create a proper balance between performance and cost.