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PSDSE: Particle Swarm Driven Design Space Exploration of Architecture and Unrolling Factors for Nested Loops in High Level Synthesis

机译:PSDSE:高级综合中嵌套循环的体系结构和展开因子的粒子群驱动设计空间探索

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A novel methodology for automated simultaneous design space exploration (DSE) of architecture and unrolling factors (UFs) for nested and single loops based application in high level synthesis (HLS) using particle swarm algorithm (named as 'PSDSE') is presented in this paper. The major contributions of the proposed methodology are as follows: (a) automated exploration of architecture and UFs using particle swarm intelligence that parallely maintain trade off between conflicting metrics of power -- performance and balance orthogonal issues of improving Quality of Result (Or) and reducing the exploration runtime for nested loops, (b) deriving a model which directly estimate the execution time of nested loop based on resource constraint and UFs without necessity of tediously unrolling the entire control and data flow graph (CDFG) for the specified UFs values in most cases. Results indicated an average improvement in QoR of >49 % and reduction in runtime of >97% compared to recent approaches.
机译:本文提出了一种新颖的方法,用于基于粒子群算法(称为“ PSDSE”)的嵌套和单循环的高层综合(HLS)应用中的体系结构和展开因子(UFs)的自动同时设计空间探索(DSE) 。拟议方法的主要贡献如下:(a)使用粒子群智能对架构和超滤器进行自动探索,该算法可并行维护权能冲突的指标之间的权衡-性能并平衡改善结果质量(Or)的正交问题,以及减少嵌套循环的探索运行时间,(b)推导一个模型,该模型可根据资源约束和UF直接估计嵌套循环的执行时间,而无需繁琐地为指定的UF值展开整个控制和数据流图(CDFG)。在大多数情况下。结果表明,与最近的方法相比,QoR的平均改善幅度大于49%,运行时间的减少幅度大于97%。

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