Efficient power macromodeling approach for heterogeneously stacked 3d ICs using Bio-geography based optimization

Faisal Siddiq and Yaseer Arafat Durrani

PLOS ONE, 2022, vol. 17, issue 2, 1-25

Abstract: Low-power consumption has been always a crucial design constraint for an efficient intellectual property based three-dimensional multi-core system that cannot be ignored easily. As the complexity increases due to the number of cores/stacks/ layers in 3D digital systems, the challenges to handle power can be more difficult at a high abstraction level. Therefore, the low-power approach gives designers an opportunity to estimate and optimize the power consumption in the early stages of design phases. The accurate power estimation through the macro-modeling approach at high-level reduces the risk of redesign cycle and turn-around time. In this research, we have presented an improved statistical macro-modeling approach that estimates power through statistical characteristics of randomly generated input patterns by using Biogeography Based Optimization. These input patterns propagate signals into an IP-based 3D digital test system. In experiments, the test system is based on four 8 to 32- bits heterogeneous cores. The response of the power is monitored by applying the well-known Monte Carlo Simulation technique. The entire power estimation method is performed in two major steps. First, the average power is estimated for an IP-based individual core. Second, the average power for bus-based Through-Silicon-Via is estimated. Finally, the cores and B-TSVs are integrated together to construct a 3D system. Then the average power for complete test systems is estimated. The experimental results of the statistical power macro-model are compared with the commercial Electronic Design Automation power simulator at the operating frequency of 100 MHz. The average percentage error of the test system is calculated as 8.65%. For the validation of these results, the statistical error analysis is additionally performed and reveals that our proposed macro-model is accurate in terms of percentage of error with a feasible amount of time.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0264181

DOI: 10.1371/journal.pone.0264181

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