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PDF(6548 KB)
PDF(6548 KB)
面向大规模FPGA的粗粒度并行布线方法研究
Research on coarse-grained parallel routing method for large-scale FPGAs
针对大规模FPGA布线过程中存在的资源开销与内存占用过大、布线算法求解效率低等问题,提出了一种资源友好型的面向大规模FPGA的粗粒度并行布线方法。首先,提出了非侵入式的数据优化技术,以减少因布线资源图而导致的资源开销与内存占用,解决因FPGA规模增大而导致的内存空间爆炸问题,为布线方法提供数据基座。其次,提出了自适应负载均衡以及高扇出线网划分技术,以解决粗粒度并行布线方法并行度低的问题,提升布线方法求解效率。实验结果表明,所提出的面向大规模FPGA的粗粒度并行布线方法可以在降低资源消耗与内存占用90%的情况下,获得3.18倍的运行时间加速比,而不会对线长与关键路径实验等性能指标造成影响。
Aiming at the problems such as excessive resource overhead, high memory consumption, and low routing efficiency in the routing process of large-scale FPGAs, a resource-friendly coarse-grained parallel routing method tailored for large-scale FPGAs is proposed. First, a non-intrusive data optimization technique is proposed to reduce the resource overhead and memory consumption caused by the routing resource graph, addressing the memory explosion problem resulting from the increasing scale of FPGAs and providing a data foundation for the routing method. Second, adaptive load balancing and high-fanout net partitioning techniques are introduced to tackle the low parallelism in coarse-grained parallel routing, thereby improving the overall routing efficiency. The experimental results show that the proposed coarse-grained parallel routing method for large-scale FPGAs can achieve a 3.18× speedup in runtime while reducing resource and memory consumption by 90%, without compromising performance metrics such as wirelength and critical path delay.
现场可编程门阵列 / 粗粒度并行布线 / 自适应负载均衡 / 布线资源图 / 非侵入式数据优化
FPGA / coarse-grained parallel routing / adaptive load balancing / routing resource graph / non-intrusive data optimization
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随着大规模集成电路器件复杂度与容量的不断提升,现场可编程门阵列(Field Programmable Gate Array, FPGA)以高度的并行、可定制和可重构的特性得到了广泛的关注与应用. 在制约FPGA发展的众多因素中,最为关键的便是电子设计自动化(Electronic Design Automation, EDA)技术,作为FPGA EDA流程中的关键环节,布局和布线技术的研究对于FPGA的重要性不言而喻. 本文综述了面向FPGA的布局和布线技术,包括基于划分的布局、基于启发式的布局、基于解析式的布局、FPGA串行布线和FPGA并行布线等技术,分析对比了不同技术方法的优缺点,在此基础上,本文还展望了未来FPGA布局和布线技术的发展趋势,将为FPGA未来健康可持续的发展提供有力支撑.
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