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Algorithm and Architecture Design of FAST-C Image Corner Detection Engine

First-in-first-out (FIFO) line buffers occupy considerable logic gates and consume significant power of the feature from accelerated segment test (FAST) image corner detection engine. In this study, a FAST-Chip (FAST-C) engine is proposed to eliminate these drawbacks. It comprises three core techniq...

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Bibliographic Details
Published in:IEEE transactions on very large scale integration (VLSI) systems 2021-04, Vol.29 (4), p.788-799
Main Authors: Lee, Yu-Hsuan, Chen, Tzu-Chieh, Liang, Hsuan-Chi, Liao, Jian-Xiang
Format: Article
Language:English
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Summary:First-in-first-out (FIFO) line buffers occupy considerable logic gates and consume significant power of the feature from accelerated segment test (FAST) image corner detection engine. In this study, a FAST-Chip (FAST-C) engine is proposed to eliminate these drawbacks. It comprises three core techniques: arch pixel estimation (APE), hardware-efficient line buffer (HLB), and interleaving-based parallel access (IPA). APE can save three line buffers using gradient information for pixel prediction. With APE, the number of line buffers can be saved by 33%. HLB effectively organizes static random access memories (SRAMs) as line buffers to store reference pixels rather than FIFO. It demands less power and fewer logic gates. IPA makes the FAST-C engine have regular data paths and smooth data scheduling, keeping hardware throughput. Experiment results show that the accuracy difference between FAST and FAST-C is as minor as 2.29%. The proposed FAST-C engine is implemented using the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18- \mu \text{m} CMOS process with a logic gate count of 223k, where the line buffers are included. Its frequency is 613 MHz with a power consumption of 1.18 W. Its maximum throughput can sufficiently support 3840\times 2.160 (4k) @ 60 frames per second (fps). Compared with sophisticated FAST engines, the FAST-C engine presents outstanding energy efficiency (EE) and area efficiency (AE).
ISSN:1063-8210
1557-9999
DOI:10.1109/TVLSI.2020.3031294