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LEAKAGE POWER REDUCTION THROUGH DUAL V_th ASSIGNMENT CONSIDERING THRESHOLD VOLTAGE VARIATION

Tsinghua National Laboratory for Information Science and Technology, Circuits and Systems Division, Electronics Engineering Department, Tsinghua University, Beijing, 100084, China

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XUKAI SHEN

Tsinghua National Laboratory for Information Science and Technology, Circuits and Systems Division, Electronics Engineering Department, Tsinghua University, Beijing, 100084, China

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RONG LUO

Tsinghua National Laboratory for Information Science and Technology, Circuits and Systems Division, Electronics Engineering Department, Tsinghua University, Beijing, 100084, China

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HUAZHONG YANG

Tsinghua National Laboratory for Information Science and Technology, Circuits and Systems Division, Electronics Engineering Department, Tsinghua University, Beijing, 100084, China

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https://doi.org/10.1142/S0218126609005642Cited by:1 (Source: Crossref)

Abstract

With technology scaling, leakage power has become an important part of the total power consumption, affecting both yields and lifetime of digital circuits. Dual V_th assignment, which was an effective method to reduce leakage power in the past, is also effective in today's technologies with certain modifications. In this paper, based on our statistical timing analysis (SSTA) framework, we presented a dual V_th assignment method which can effectively reduce the leakage power even in the presence of large V_th variation. Besides, taking the correlation between gates into account, we propose a novel statistical DAG pruning method to speed up the dual V_th assignment algorithm. Experimental results show that statistical dual V_th assignment can reduce on average 95% and 40% more leakage current compared with the conventional static method, without affecting the performance constraints under 180 nm and 90 nm technology nodes. Also our statistical DAG pruning method can reduce 30% gates in the circuit on average and save up to 50% of the total runtime in the 90 nm technology node.

This work was supported by grants from 863 program of China (No. 2009AA01Z130), and NSFC (No. 60870001, 90207002) and TNList Cross-discipline Foundation.

Keywords:

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