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Reconfigurable Multi-Access Pattern Vector Memory For Real-Time ORB Feature Extraction
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DOI：10.1109/ISCAS51556.2021.9401698.png)
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Abstract
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This work presents an on-chip memory subsystem envisioned for real-time applications performing Oriented FAST and Rotated Brief (ORB) feature extraction for Simultaneous Localization and Mapping (SLAM) systems. For autonomous navigation of battery-powered devices, feature-based SLAM is a computationally frugal alternative to direct methods. This paper thoroughly analyses ORB multiple memory access patterns, exploring possible systematic parallelism and hardware-biased algorithmic enhancements, alleviating requirements on bandwidth and reducing redundant accesses. Enabling those, a suitable multi-bank parallel memory featuring run-time reconfigurable address generation, image allotment, and close-to-memory data-shuffling is proposed. As case study, a 30 Frames-Per-Second (FPS) VGA-resolution ORB-capable 8-bank memory is evaluated using 22 FDX technology, running at 909 MHz, with a negligible area overhead of 0.3%, reducing operand accesses between 54 - 160x relative to Sudoku-like and scalar memories.
Keywords:
vision-based SLAM
Feature Extraction
ORB
programmable multiple memory access patterns
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