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Algorithms for non-Hanan-based optimization for VLSI interconnect under a higher-order AWE model

机译:高阶AWE模型下基于VLSI互连的非基于Hanan的优化算法

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摘要

To improve the performance of critical nets where both timing and wire resources are stringent, we integrate buffer insertion and driver sizing separately with non-Hanan optimization and propose two algorithms: simultaneous buffer insertion and non-Hanan optimization (BINO) and full-plane AWE routing with driver sizing (FAR-DS). For BINO, we consider the realistic situation that buffer locations are restricted to a limited set of available spaces after cell placement. The objective of BINO is to minimize a weighted sum of wire and buffer costs subject to timing constraints. To achieve this objective, we suggest a greedy algorithm that considers two operations independently: iterative buffer insertion and iterative buffer deletion. Both are conducted simultaneously with non-Hanan optimization until the improvement is exhausted. For FAR-DS, we investigate the curvature property of the sink delay as a function of both connection location and driver stage ratio in a two-dimensional (2-D) space. The objective of FAR-DS is to minimize a weighted sum of wire and driver cost while ensuring that the timing constraints are satisfied. Based on the curvature property, we search for the optimal solution in the continuous 2-D space. In both BINO and FAR-DS, a fourth-order AWE delay model is employed to assure the quality of optimization. Experiments of BINO and FAR-DS on both integrated circuit and MCM technologies showed significant cost reductions compared with SERT and MVERT in addition to making the interconnect to satisfy timing constraints.
机译:为了提高时序和布线资源都严格的关键网络的性能,我们将缓冲区插入和驱动程序大小调整与非汉南优化分开集成,并提出了两种算法:同时缓冲区插入和非汉南优化(BINO)和全平面AWE通过驱动程序调整大小(FAR-DS)进行路由。对于BINO,我们考虑了一个现实情况,即在单元放置后,缓冲区位置被限制为一组有限的可用空间。 BINO的目标是最大程度地减少受时序限制的电线和缓冲器成本的加权总和。为了实现此目标,我们建议一种贪婪算法,该算法独立考虑两个操作:迭代缓冲区插入和迭代缓冲区删除。两者都与非Hanan优化同时进行,直到穷尽为止。对于FAR-DS,我们研究了二维(2-D)空间中下沉延迟的曲率特性,它是连接位置和驱动级比的函数。 FAR-DS的目标是在确保满足时序约束的同时,将导线和驱动器成本的加权总和最小化。基于曲率属性,我们在连续二维空间中搜索最优解。在BINO和FAR-DS中,均采用四阶AWE延迟模型来确保优化的质量。 BINO和FAR-DS在集成电路和MCM技术上的实验表明,除了使互连满足时序约束之外,与SERT和MVERT相比,成本大大降低。

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