Exhaustive Data Path Optimization in High-Level Synthesis through Area Improvement

Rahimi, Abbas; Mohammadi, Siamak; Ranjbar, Saei

doi:10.1109/iccit.2009.34

Cited by 1 publication

(3 citation statements)

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“…To show the efficiency of our refinement algorithm, our previous algorithm for data path optimization based on heuristic [12] is compared with our new algorithm based on dynamic programming. Experimental results of area optimization in heuristic method and dynamic programming method are shown in the fourth and the fifth columns of Table 2 respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Our major contribution in comparison to [12] lies in basic block extension section and basic block selection and optimization section. Our method tries to reduce the number of resources to maximize the resource optimization, while meeting performance constraint.…”

Section: Methodsmentioning

confidence: 99%

“…The authors of this paper presented a heuristic algorithm for data path optimization in high-level synthesis through the distribution of the operations on different control steps of each basic block in order to reduce area of the basic blocks [12]. Better resource optimization is achieved by using Knapsack Problem instead of heuristic method, which will be shown through this work.…”

Section: Related Workmentioning

confidence: 96%

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Data path refinement algorithm in high-level synthesis based on dynamic programming

Rahimi

Mohammadi

Foroughi

2009

2009 International Conference on Innovations in Information Technology (IIT)

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As CMOS technology scales down into the deep-submicron domain, the cost of design, complexity and customization for Systems-On-Chip (SoCs) is rapidly increasing due to the inefficiency of traditional CAD tools. In this paper we present a new interactive refinement algorithm in high-level synthesis, based on dynamic programming, which maximizes resource optimization in data path. We start by quantifying the properties of the given application C code in terms of control data flow graph (CDFG), available parallelism and other metrics. We then apply designer guided constraints to a data path refinement algorithm for an initial data path. It attempts to reduce the number of the most expensive components while meeting the constraints. The experimental results show that not only the refined data path outperforms data paths refined by other heuristic methods, but also presents lower cost, less overhead and can be generated in less time.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 96%

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