Special Issue Paper
Model-driven physical-design automation for FPGAs: fast prototyping and legacy reuse
Article first published online: 6 MAR 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Software: Practice and Experience
Special Issue: Special issue on International Workshop on Smalltalk Technologies 2011.
Volume 44, Issue 4, pages 455–482, April 2014
How to Cite
Teodorov, C. and Lagadec, L. (2014), Model-driven physical-design automation for FPGAs: fast prototyping and legacy reuse. Softw: Pract. Exper., 44: 455–482. doi: 10.1002/spe.2190
- Issue published online: 4 MAR 2014
- Article first published online: 6 MAR 2013
- Manuscript Accepted: 17 SEP 2012
- Manuscript Revised: 28 AUG 2012
- Manuscript Received: 31 OCT 2011
The current integrated circuit technologies are approaching their physical limits in terms of scaling and power consumption, in this context, the electronic design automation (EDA) industry is pushed towards solving ever more challenging problems in terms of performance, scalability and adaptability. Meeting these constraints needs innovation at both the algorithmic and the methodological level. Amongst academic EDA tools, Madeo toolkit has been targeting field-programmable gate array (FPGA) design-automation at the logic and the physical level since the late 1990s. As many other long-living software, despite embedding valuable legacy, Madeo exhibits unwanted characteristics that penalize evolution and render the automation problems even more difficult. This study presents a methodological approach to physical-design automation relying on model-driven engineering, which is illustrated through the incremental redesign of the Madeo framework. A benefit of this approach is the emergence of a common vocabulary to describe the EDA domain in an FPGA scope. A second advantage is the isolation of the optimization algorithms from the structural domain models. However, the main asset is the possibility to re-inject into the newly designed toolkit most of the legacy code. The redesigned framework is compared with and scored against initial code-base, and demonstrates a regression-free remodeling of the environment with net improvements in terms of size and complexity metrics. As a consequence, the evolution capability is back on stage, and the domain-space exploration widens to the algorithmic axis. Copyright © 2013 John Wiley & Sons, Ltd.