基于SSTA的透明锁存电路分组优化

Min-Sik Gong, H. Zhou, Jun Tao, Xuan Zeng
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引用次数: 3

摘要

随着工艺变化的不断增加,分形已成为提高芯片制造价值的重要技术,特别是在广泛使用透明锁存器的高性能微处理器中。本文在考虑测试成本的前提下,提出并求解了透明锁存电路的分箱优化问题,该问题决定了分箱边界及其测试顺序,从而使效益最大化。将该问题分解为三个子问题,依次求解。首先,为了计算透明锁存电路的时钟周期分布,提出了一种基于稀疏网格技术的广义随机配置法(gSCM)的采样SSTA方法。通过求解约束图中的最小周期比问题,求出每个采样点上的最小时钟周期。其次,提出了一种贪婪算法,通过迭代分配每个边界到其最优位置来最大化销售利润。然后,基于字母树,采用运行时间为O(n log n)的最优算法生成bin边界的最优测试顺序,以最小化测试成本。采用65纳米技术的ISCAS'89系列基准测试显示,平均利润提高6.69%,成本降低14.00%。结果还表明,与蒙特卡罗模拟相比,所提出的SSTA方法的误差为0.70%,平均加速为110X。分类和主题描述:J.6[计算机辅助工程]:计算机辅助设计一般术语:设计,算法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Binning optimization based on SSTA for transparently-latched circuits
With increasing process variation, binning has become an important technique to improve the values of fabricated chips, especially in high performance microprocessors where transparent latches are widely used. In this paper, we formulate and solve the binning optimization problem that decides the bin boundaries and their testing order to maximize the benefit (considering the test cost) for a transparently-latched circuit. The problem is decomposed into three sub-problems which are solved sequentially. First, to compute the clock period distribution of the transparently-latched circuit, a sample-based SSTA approach is developed which is based on the generalized stochastic collocation method (gSCM) with Sparse Grid technique. The minimal clock period on each sample point is found by solving a minimal cycle ratio problem in the constraint graph. Second, a greedy algorithm is proposed to maximize the sales profit by iteratively assigning each boundary to its optimal position. Then, an optimal algorithm of O(n log n) runtime is used to generate the optimal testing order of bin boundaries to minimize the test cost, based on alphabetic tree. Experiments on all the ISCAS'89 sequential benchmarks with 65-nm technology show 6.69% profit improvement and 14.00% cost reduction in average. The results also demonstrate that the proposed SSTA method achieves an error of 0.70% and speedup of 110X in average compared with the Monte Carlo simulation. Categories and Subject Descriptors: J.6 [Computer-Aided Engineering]: Computer-Aided Design General Terms: Design, Algorithms
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