Design validation of RTL circuits using evolutionary swarm intelligence

Min Li, K. Gent, M. Hsiao
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引用次数: 34

Abstract

In this paper, we present BEACON, a Branch-oriented Evolutionary Ant Colony OptimizatioN method which is a bio-inspired meta-heuristic for design validation and functional test generation. BEACON combines an evolutionary search technique with Ant Colony Optimization (ACO) for improved search capability. BEACON first cross-compiles the Verilog circuit source to a C++ base for fast simulation. Then, it profiles the code, keeping track of each branch and the number of times it has been visited in a database. Branch coverage provides a very useful metric for exploring the design, especially visiting the most critical states, including corner states, in the design. At 100% branch coverage, we can conclude that every control state described in the RTL has been visited. Thus, during execution, BEACON trims highly visited branches from the search and focuses the search on rarely occurring branches and paths. This approach gives a significant performance boost while maintaining a high level of coverage. Experimental results show that BEACON is able to achieve very high branch coverages with a fraction of computational cost. In addition, previous hard-to-reach corner states in the ITC99 benchmarks have now been reached by BEACON. New states can also be discovered from the RTL descriptions. For many circuits, one to two orders of magnitude speedups over existing methods have been achieved.
基于进化群智能的RTL电路设计验证
在本文中,我们提出了一种基于分支的进化蚁群优化方法BEACON,它是一种启发生物的设计验证和功能测试生成元启发式方法。BEACON将进化搜索技术与蚁群优化(蚁群优化)相结合,提高了搜索能力。BEACON首先将Verilog电路源交叉编译为c++基础,以实现快速仿真。然后,它分析代码,跟踪每个分支及其在数据库中被访问的次数。分支覆盖率为探索设计提供了一个非常有用的度量,特别是访问设计中最关键的状态,包括角落状态。在100%的分支覆盖率下,我们可以得出结论,RTL中描述的每个控制状态都被访问了。因此,在执行期间,BEACON会从搜索中删除访问次数较多的分支,并将搜索重点放在很少出现的分支和路径上。这种方法在保持高覆盖率的同时显著提高了性能。实验结果表明,该算法能够以很小的计算成本实现很高的分支覆盖率。此外,以前ITC99基准中难以达到的角落状态现在已由BEACON达到。还可以从RTL描述中发现新的状态。对于许多电路,已经实现了比现有方法一到两个数量级的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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