A 32x32 Time-Domain Wavefront Computing Accelerator for Path Planning and Scientific Simulations

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A 32x32 Time-Domain Wavefront Computing Accelerator for Path Planning and Scientific Simulations
Title:
A 32x32 Time-Domain Wavefront Computing Accelerator for Path Planning and Scientific Simulations
Journal Title:
2021 IEEE Custom Integrated Circuits Conference (CICC)
Publication Date:
17 May 2021
Citation:
Yu, C., Su, Y., Lee, J., Chai, K., Kim, B. (2021). A 32x32 Time-Domain Wavefront Computing Accelerator for Path Planning and Scientific Simulations. 2021 IEEE Custom Integrated Circuits Conference (CICC). https://doi.org/10.1109/cicc51472.2021.9431448
Abstract:
Recent hardware accelerators based on FPGA –[2] and ASIC –[4] have demonstrated high throughput and energy-efficient processing of path planning for applications, including the manipulation of arm-robots on a high-resolution map [1] and the autonomous navigation of battery-powered micro-robots [4]. The accelerators have focused on demonstrating the modified shortest path planning algorithms, such as extended-stride A∗ [1] and dual-tree rapid-exploring random tree (RRT) [4], and achieved much higher throughput and energy efficiency (two-to-three orders of magnitude higher) than traditional implementations with CPU and GPU. A two-dimensional processing element (PE) array [5] was proposed as an alternative computing paradigm for searching the shortest path based on the expansion of wavefront in the time-domain. The time-domain delay accumulation was used for finding the shortest paths by accumulating time delays while propagating through the PEs that correspond to the pixels in a two-dimensional searching space. However, it still follows the existing A∗ algorithm using an extra gradient control circuit with resistor ladders to estimate the remaining distances, resulting in inaccurate results (i.e. sub-optimal paths) with extra hardware and energy overhead.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2022 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.
ISSN:
2152-3630
ISBN:
978-1-7281-7582-9
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