AI Article Synopsis

  • FPGAs' reconfigurable nature offers benefits for adaptive systems-on-chip but adds complexity to the design process.
  • The paper introduces a model-driven design flow using an enhanced MARTE UML profile to simplify reconfigurable system design, compatible with both centralized and decentralized decision-making.
  • Validation through a video watermarking application demonstrates that the approach allows for efficient hardware solutions with a favorable balance of resource use, power consumption, execution speed, and image quality, thanks to design automation.

Article Abstract

The reconfigurable feature of FPGAs (Field-Programmable Gate Arrays) has made them a very attractive solution for implementing adaptive systems-on-chip. However, this implies additional design tasks to handle system reconfiguration and control, which increases design complexity. To address this issue, this paper proposes a model-driven design flow that guides the designer through the description of the different elements of a reconfigurable system. It is based on high-level modeling using an extended version of the MARTE (Modeling and Analysis of Real-Time and Embedded systems) UML (Unified Modeling Language) profile. Both centralized and decentralized reconfiguration decision-making solutions are possible with the proposed flow, allowing it to adapt to various reconfigurable systems constraints. It also integrates the IP-XACT standard (standard for the description of electronic Intellectual Properties), allowing the designer to easily target different technologies and commercial FPGAs by reusing both high-level models and actual IP-XACT hardware components. At the end of the flow, the implementation code is generated automatically from the high-level models. The proposed design flow was validated through a reconfigurable video watermarking application as a case study. Experimental results showed that the generated system allowed a good trade-off between resource usage, power consumption, execution time, and image quality compared to static implementations. This hardware efficiency was achieved in a very short time thanks to the design acceleration and automation offered by model-driven engineering.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964074PMC
http://dx.doi.org/10.3390/mi14020481DOI Listing

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