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Timing Aware Partitioning for Multi-FPGA based Logic Simulation using Top-down Selective Flattening

机译:使用自上而下的选择性展平对基于Multi-FPGA的逻辑仿真进行时序感知分区

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摘要

In order to accelerate logic simulation, it is highly beneficial to simulate the circuit design on FPGA hardware. However, limited hardware resources on FPGAs prevent large designs from being implemented on a single FPGA. Hence there is a need to partition the design and simulate it on a multi-FPGA platform. In contrast to existing FPGA-based post-synthesis partitioning approaches which first completely flatten the circuit and then possibly perform bottom-up clustering, we perform a selective top-down flattening and thereby avoid the potential netlist blowup. This also allows us to preserve the design hierarchy to guide the partitioning and to make subsequent debugging easier. Our approach analyzes the hierarchical design and selectively flattens instances using two metrics based on slack. The resulting partially flattened netlist is converted to a hypergraph, partitioned using a public domain partitioner (hMetis), and reconverted back to a plurality of FPGA netlists, one for each FPGA of the FPGA-based accelerated logic simulation platform. We compare our approach with a partitioning approach that operates on a completely flattened netlist. Static timing analysis was performed for both approaches, and over 15 examples from the OpenCores project, our approach yields a 52% logic simulation speedup and about 0.74x runtime for the entire flow, compared to the completely flat approach. The entire tool chain of our approach is automated in an end-to-end flow from hierarchy extraction, selective flattening, partitioning, and netlist reconstruction. Compared to an existing method which also performs slack-based partitioning of a hierarchical netlist, we obtain a 35% simulation speedup.
机译:为了加速逻辑仿真,在FPGA硬件上仿真电路设计非常有益。但是,由于FPGA上有限的硬件资源,无法在单个FPGA上实现大型设计。因此,需要对设计进行分区并在多FPGA平台上对其进行仿真。与现有的基于FPGA的后综合划分方法相反,该方法首先将电路完全扁平化,然后可能执行自下而上的聚类,我们执行选择性的自上而下的扁平化,从而避免了潜在的网表爆炸。这也使我们能够保留设计层次结构,以指导分区并简化后续调试。我们的方法分析了分层设计,并使用基于松弛的两个指标选择性地对实例进行了展平。生成的部分扁平化的网表被转换为超图,使用公共域分区器(hMetis)进行分区,然后重新转换为多个FPGA网表,其中一个针对基于FPGA的加速逻辑仿真平台的每个FPGA。我们将我们的方法与在完全扁平化的网表上运行的分区方法进行了比较。两种方法均进行了静态时序分析,与完全平坦的方法相比,我们的方法从OpenCores项目中获得了15个以上的示例,与整个流程相比,我们的方法在整个流程中实现了52%的逻辑仿真速度提高和约0.74倍的运行时间。我们的方法的整个工具链在从层次结构提取,选择性展平,分区和网表重构的端到端流程中都是自动化的。与也执行基于松弛的分层网表分区的现有方法相比,我们获得了35%的仿真速度。

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