{zoltanmann, a n d r a s o r b a n , pappd)@cs.bme.hu -One of the most crucial steps in the design of embedded systems k hardware-software panitioning, i.e. deciding which components of the system should be implementcd in hardware and which ones in software. In this paper, d#erent versions of the partitioning problem are defined, corresponding Io real-lime systems, and cost-constrained systems, respectively, The authors provide a forntal mathematic analysis of the complex;@ of the problems: it is proven that they are NP-hard in the general case, and some efficiently solvable special cases are also presented. A n
ILP (integer linearprogromming) based approach is presented thatcan solve the problem optimally men for quite big systems, and a generic algorithm (CA) that finds near-optimal solutions for ewen larger systems. A special@ of the W is that non-valid individ#ak are ulso allowed, butpunished by thefitnessfunction.
Kevwords-genetic algorithm, graph partitioning, hardwardsofware codesign, hardwardsofware partitioning, inIeger linear programnting
One of the most crucial steps in the design of embedded systems is hardware/software partitioning, i.e. deciding which components of the system should be implemented in hardware and which ones in software. Most formulations of the hardware/software partitioning problem are N P-hard, so the majority of research eorts on hardware/software partitioning has focused on developing ecient heuristics.This paper considers the combinatorial structure behind hardware/software partitioning. Two similar versions of the partitioning problem are dened, one of which turns out to be N P-hard, whereas the other one can be solved in polynomial time. This helps in understanding the real cause of complexity in hardware/software partitioning. Moreover, the polynomial-time algorithm serves as the basis for a highly ecient novel heuristic for the N P-hard version of the problem. Unlike general-purpose heuristics such as genetic algorithms or simulated annealing, this heuristic makes use of problem-specic knowledge, and can thus nd high-quality solutions rapidly. Moreover, it has the unique characteristic that it also calculates lower bounds on the optimum solution. It is demonstrated on several benchmarks and also large random examples that the new algorithm clearly outperforms other heuristics that are generally applied to hardware/software partitioning.
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