【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(lái)越緊密，傳統(tǒng)設(shè)計(jì)方法已經(jīng)無(wú)法滿足越來(lái)越復(fù)雜的設(shè)計(jì)需要。為了克服傳統(tǒng)設(shè)計(jì)方法的不足，嵌入式設(shè)計(jì)者們提出并逐漸完善軟硬件協(xié)同設(shè)計(jì)方法。軟硬件劃分技術(shù)是軟硬件協(xié)同設(shè)計(jì)中的關(guān)鍵技術(shù)之一，它是指在系統(tǒng)設(shè)計(jì)時(shí)，將系統(tǒng)各項(xiàng)任務(wù)劃分到軟件或者硬件上實(shí)現(xiàn)，劃分結(jié)果直接決定系統(tǒng)設(shè)計(jì)的優(yōu)劣。因此研究嵌入式系統(tǒng)的軟硬件劃分技術(shù)，，具有十分重要的應(yīng)用價(jià)值。 目前對(duì)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(rèn)了系統(tǒng)中的每項(xiàng)任務(wù)有一種軟件實(shí)現(xiàn)方式和一種硬件方式，而忽略了一項(xiàng)任務(wù)可能存在多種硬件實(shí)現(xiàn)方式的可能性，即硬件多路映射問(wèn)題。 本文對(duì)硬件多路映射問(wèn)題進(jìn)行研究，并比較局部搜索算法BUB與遺傳算法，通過(guò)實(shí)驗(yàn)發(fā)現(xiàn)遺傳算法能夠得到更好的劃分結(jié)果。在進(jìn)一步研究過(guò)程中，針對(duì)遺傳算法局部搜索能力較弱的情況以及硬件多路映射問(wèn)題的特點(diǎn)，本文采用強(qiáng)化學(xué)習(xí)方法設(shè)計(jì)了變異算子以代替標(biāo)準(zhǔn)遺傳算法中隨機(jī)變異的方式，使不同的染色體能夠自適應(yīng)的選擇的動(dòng)作進(jìn)化，提高了遺傳算法的局部搜索能力和收斂速度。經(jīng)實(shí)例驗(yàn)證，改進(jìn)的遺傳算法的收斂速度和最優(yōu)解都優(yōu)于標(biāo)準(zhǔn)遺傳算法。多次運(yùn)行表明該算法具有較強(qiáng)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(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP368.1;TP18

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