Common path pessimism removal in static timing analysis

Static timing analysis is a key process to guarantee timing closure for modern IC designs. However, additional pessimism can significantly increase the difficulty to achieve timing closure. Common path pessimism removal (CPPR) is a prevalent step to achieve accurate timing signoff. To speed up the existing exhaustive exploration on all paths in a design, this thesis introduces a fast multi-threading timing analysis for removing common path pessimism based on block-based static timing analysis. Experimental results show that the proposed method has faster runtime in removing excess pessimism from clock paths. --Abstract, page iii

Robust Estimation of 3D Human Poses from a Single Image

Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the lack of depth information. Moreover, current 2D pose estimators are usually inaccurate which may cause errors in the 3D estimation. We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons. (ii) We enforce limb length constraints to eliminate anthropomorphically implausible skeletons. (iii) We estimate a 3D pose by minimizing the $L_1$-norm error between the projection of the 3D pose and the corresponding 2D detection. The $L_1$-norm loss term is robust to inaccurate 2D joint estimations. We use the alternating direction method (ADM) to solve the optimization problem efficiently. Our approach outperforms the state-of-the-arts on three benchmark datasets