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Hephaestus: Codesigning and Automating 3D Image Registration on Reconfigurable Architectures

ACM Transactions on Embedded Computing Systems, ISSN: 1558-3465, Vol: 22, Issue: 5 s, Page: 1-24
2023
  • 4
    Citations
  • 0
    Usage
  • 4
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    4
  • Captures
    4
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

Recent Findings from Polytechnic University Milan Provides New Insights into Embedded Computing Systems (Hephaestus: Codesigning and Automating 3d Image Registration On Reconfigurable Architectures)

2023 NOV 13 (NewsRx) -- By a News Reporter-Staff News Editor at Computer News Today -- A new study on Computers - Embedded Computing Systems

Article Description

Healthcare is a pivotal research field, and medical imaging is crucial in many applications. Therefore finding new architectural and algorithmic solutions would benefit highly repetitive image processing procedures. One of the most complex tasks in this sense is image registration, which finds the optimal geometric alignment among 3D image stacks and is widely employed in healthcare and robotics. Given the high computational demand of such a procedure, hardware accelerators are promising real-time and energy-efficient solutions, but they are complex to design and integrate within software pipelines. Therefore, this work presents an automation framework called Hephaestus that generates efficient 3D image registration pipelines combined with reconfigurable accelerators. Moreover, to alleviate the burden from the software, we codesign software-programmable accelerators that can adapt at run-time to the image volume dimensions. Hephaestus features a cross-platform abstraction layer that enables transparently high-performance and embedded systems deployment. However, given the computational complexity of 3D image registration, the embedded devices become a relevant and complex setting being constrained in memory; thus, they require further attention and tailoring of the accelerators and registration application to reach satisfactory results. Therefore, with Hephaestus, we also propose an approximation mechanism that enables such devices to perform the 3D image registration and even achieve, in some cases, the accuracy of the high-performance ones. Overall, Hephaestus demonstrates 1.85× of maximum speedup, 2.35× of efficiency improvement with respect to the State of the Art, a maximum speedup of 2.51× and 2.76× efficiency improvements against our software, while attaining state-of-the-art accuracy on 3D registrations.

Bibliographic Details

Giuseppe Sorrentino; Marco Venere; Davide Conficconi; Eleonora D'Arnese; Marco Domenico Santambrogio

Association for Computing Machinery (ACM)

Computer Science

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