【摘要】：隨著嵌入式系統(tǒng)的不斷發(fā)展，軟件和硬件結(jié)合的越來越緊密，傳統(tǒng)設計方法已經(jīng)無法滿足越來越復雜的設計需要。為了克服傳統(tǒng)設計方法的不足，嵌入式設計者們提出并逐漸完善軟硬件協(xié)同設計方法。軟硬件劃分技術是軟硬件協(xié)同設計中的關鍵技術之一，它是指在系統(tǒng)設計時，將系統(tǒng)各項任務劃分到軟件或者硬件上實現(xiàn)，劃分結(jié)果直接決定系統(tǒng)設計的優(yōu)劣。因此研究嵌入式系統(tǒng)的軟硬件劃分技術，，具有十分重要的應用價值。 目前對軟硬件劃分的研究主要集中在二元(binary partitioning)劃分上，這種劃分方式默認了系統(tǒng)中的每項任務有一種軟件實現(xiàn)方式和一種硬件方式，而忽略了一項任務可能存在多種硬件實現(xiàn)方式的可能性，即硬件多路映射問題。 本文對硬件多路映射問題進行研究，并比較局部搜索算法BUB與遺傳算法，通過實驗發(fā)現(xiàn)遺傳算法能夠得到更好的劃分結(jié)果。在進一步研究過程中，針對遺傳算法局部搜索能力較弱的情況以及硬件多路映射問題的特點，本文采用強化學習方法設計了變異算子以代替標準遺傳算法中隨機變異的方式，使不同的染色體能夠自適應的選擇的動作進化，提高了遺傳算法的局部搜索能力和收斂速度。經(jīng)實例驗證，改進的遺傳算法的收斂速度和最優(yōu)解都優(yōu)于標準遺傳算法。多次運行表明該算法具有較強的穩(wěn)定性，具有很好的效果。
[Abstract]:With the development of embedded system and the combination of software and hardware, the traditional design method can not meet the needs of more and more complex design. In order to overcome the shortcomings of traditional design methods, embedded designers put forward and gradually improve the hardware and software co-design method. Software / hardware partitioning technology is one of the key technologies in hardware and software co-design. It means that the tasks of the system are divided into software or hardware when the system is designed, and the partition results directly determine the merits and demerits of the system design. Therefore, it is very important to study the software and hardware partitioning technology of embedded system. At present, the research on software and hardware partitioning is mainly focused on binary (binary partitioning) partitioning, which acquires a software implementation mode and a hardware mode for each task in the system. It ignores the possibility that a task may have a variety of hardware implementations, that is, the problem of hardware multiplexing. In this paper, the hardware multiplex mapping problem is studied, and the local search algorithm BUB is compared with the genetic algorithm. The experimental results show that the genetic algorithm can get better partition results. In the process of further study, in view of the weak local search ability of genetic algorithm and the characteristics of hardware multipath mapping, this paper designs a mutation operator to replace the random mutation in the standard genetic algorithm by reinforcement learning. The local search ability and convergence speed of genetic algorithm are improved by making different chromosomes self-adaptively selective motion evolution. An example shows that the convergence speed and optimal solution of the improved genetic algorithm are better than that of the standard genetic algorithm. Multiple runs show that the algorithm has strong stability and good effect.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP368.1;TP18

【參考文獻】

相關期刊論文 前10條

1 鄒誼,莊鎮(zhèn)泉,李斌;基于量子遺傳算法的嵌入式系統(tǒng)軟硬件劃分算法[J];電路與系統(tǒng)學報;2004年05期

2 王春玲;;流水線技術在基于FPGA的DSP運算中的應用研究[J];電子技術;2009年06期

3 羅勝欽;馬蕭蕭;陸憶;;基于改進的NSGA遺傳算法的SOC軟硬件劃分方法[J];電子學報;2009年11期

4 邢冀鵬;鄒雪城;劉政林;陳毅成;;基于混沌優(yōu)化算法的軟硬件劃分[J];華中科技大學學報(自然科學版);2006年11期

5 熊志輝;李思昆;陳吉華;;具有初始信息素的螞蟻尋優(yōu)軟硬件劃分算法[J];計算機研究與發(fā)展;2005年12期

6 邢冀鵬;鄒雪城;劉政林;陳毅成;;K均值聚類和模擬退火融合的軟硬件劃分[J];計算機工程與應用;2006年16期

7 羅莉;夏軍;何鴻君;劉瀚;;一種有效的面向多目標軟硬件劃分的遺傳算法[J];計算機科學;2010年12期

8 盛藍平,林濤;采用啟發(fā)式分支定界的軟硬件劃分[J];計算機輔助設計與圖形學學報;2005年03期

9 李冉;郭兵;沈艷;王繼禾;伍元勝;劉云本;;基于Hopfield神經(jīng)網(wǎng)絡和禁忌搜索的軟/硬件功耗劃分[J];計算機應用;2011年03期

10 肖平;徐成;楊志邦;劉彥;;基于改進模擬退火算法的軟硬件劃分[J];計算機應用;2011年07期

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