粗粒度可重构阵列辅助RTL仿真加速器的DFG划分算法

I. Mahapatra, Utkarsh Agarwal, S. Nandy
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引用次数: 3

摘要

随着电路设计复杂性的增加,通过仿真验证这些电路也变得极具挑战性。这在集成电路设计过程中造成了瓶颈。分布式仿真是解决这一问题的一种方法,其中仿真工作负载分布在仿真所涉及的并行处理器之间。然而,为了这个目的,设计必须仔细划分。为了进行分布式仿真,迄今为止已经提出了许多高效的划分算法。这些算法主要划分门级网表或逻辑电路,并通过最小化给定负载平衡约束的切量来减少处理器间通信。在本文中,我们提出了两种不同的分区方案来进行分布式仿真。它们是:基于离散粒子群优化(DPSO)的分区算法(DPSO- pa)和有效的分区启发式算法。这些算法为最近提出的粗粒度可重构阵列辅助硬件加速器(CGRA-HA)划分数据流图(DFG)。我们还通过仔细选择初始分区集,提出了原始DPSO方法的改进版本。结果表明,基于启发式的分割算法在切边较少方面优于改进的DPSO-PA算法。
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DFG Partitioning Algorithms for Coarse Grained Reconfigurable Array Assisted RTL Simulation Accelerators
As the complexity of circuit design increases, verification of these circuits through simulation also becomes extremely challenging. This creates a bottleneck in the IC design process. Distributed simulation is one way of solving this problem where the simulation workload is distributed among the parallel processors involved in the simulation. However the design has to be carefully partitioned for this purpose. In order to perform distributed simulation, many efficient partitioning algorithms have been proposed till date. These algorithms mostly partition gate level netlist or logic circuits and reduces inter-processor communication by minimizing cutsize for a given constraint of load balance. In this paper, we present two different partitioning schemes for performing distributed simulation. They are: a Discrete Particle Swarm optimization (DPSO) based partitioning algorithm (DPSO-PA) and an effective partitioning heuristic. These algorithms partitions Data Flow Graph (DFG) for a recently proposed Coarse Grained Reconfigurable Array assisted Hardware Accelerator (CGRA-HA). We also propose an improved version of the original DPSO methodology through careful selection of initial partition sets. It is found that the proposed heuristic based partitioning algorithm outperforms the modified DPSO-PA in terms of lesser cut-edges.
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