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Genetic Algorithm-based thermal uniformity-aware X-filling to reduce peak temperature during testing

机译:基于遗传算法的热均匀性感知X填充,以降低测试期间的峰值温度

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摘要

High temperature occurs in testing of complex System-on-Chip designs and it may become a critical concern to be carefully taken into account with continual development in Very Large Scale Integration technology. Peak temperature significantly affects the reliability and the performance of the chip. So it is essential to minimize the peak temperature of the chip. Heat generation by power consumption and heat dissipation to the surrounding blocks are the two prominent factors for the peak temperature. Power consumption can be minimized by a careful mapping of don't cares in precomputed test set. However, it does not provide the solution to peak temperature minimization because the non-uniformity in spatial power distribution may create localized heating event called "hotspot." The peak temperature on the hotspot is minimized by Genetic Algorithm-based don't care filling technique that reduces the non-uniformity in spatial power distribution within the circuit under test while maintaining the overall power consumption at a lower level. Experimental results on ISCAS89 benchmark circuits demonstrate that 6%-28% peak temperature reduction can be achieved.
机译:在测试复杂系统的片上设计中出现高温,并且可能成为在非常大规模集成技术中持续开发的持续考虑的重要关注。峰值温度显着影响芯片的可靠性和性能。因此,必须最小化芯片的峰值温度。通过功耗和散热到周围块的发热是峰值温度的两个突出因素。通过仔细的映射,可以在预先计算的测试集中仔细映射来最小化功耗。然而,由于空间功率分布中的不均匀性,它不提供峰值温度最小化的解决方案可能会产生称为“热点”的局部加热事件。通过基于遗传算法的峰值温度最小化了,不关心填充技术,其降低了在测试中的电路内的空间功率分布中的不均匀性,同时保持较低级别的整体功耗。 ISCAS89基准电路上的实验结果表明,可以实现6%-28%的峰值温度降低。

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