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Inverse Gaussian distribution based timing analysis of Sub-threshold CMOS circuits

机译:基于反高斯分布的亚阈值CMOS电路时序分析

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Aggressive technology scaling and ultra low power constraints have resulted in less predictable device behavior complicating timing analysis/estimation. The traditional delay models fail to accurately capture the circuit behavior under such conditions. This paper proposes a novel highly accurate Inverse Gaussian Distribution (IGD) based delay model applicable to both combinational and sequential elements for sub-powered circuits. The IGD based delay estimation accuracy is demonstrated by evaluating multiple circuits, i.e., D Flip Flops (DFFs) + 8-bit Ripple Carry Adder, and 8-bit De-multiplexer (DEMUX) and Multiplexer (MUX). Our experiments indicate that the IGD based approach provides a high matching against HSPICE Monte Carlo simulation results, with an average error less than 1.9% and 1.2% for the two circuits, respectively, while sparing orders of magnitude simulation time. Moreover, the IGD model outperforms the traditional Gaussian Distribution (GD) model by providing 6x better average accuracy with no extra simulation time overhead. (C) 2015 Elsevier Ltd. All rights reserved.
机译:激进的技术扩展和超低功耗限制导致难以预测的设备行为,从而使时序分析/估计变得复杂。传统的延迟模型无法在这种情况下准确地捕获电路行为。本文提出了一种新颖的基于高精度高斯逆分布(IGD)的延迟模型,该模型适用于子电源电路的组合和顺序元件。通过评估多个电路(即D触发器(DFF)+ 8位纹波进位加法器以及8位解复用器(DEMUX)和多路复用器(MUX),可以证明基于IGD的延迟估计精度。我们的实验表明,基于IGD的方法与HSPICE蒙特卡洛仿真结果具有很高的匹配度,两个电路的平均误差分别小于1.9%和1.2%,同时节省了几个数量级的仿真时间。此外,IGD模型在不增加额外仿真时间开销的情况下,提供了6倍的平均精度,优于传统的高斯分布(GD)模型。 (C)2015 Elsevier Ltd.保留所有权利。

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