Tao Huang

Bio: Tao Huang is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Steiner tree problem & Local search (optimization). The author has an hindex of 11, co-authored 12 publications receiving 352 citations. Previous affiliations of Tao Huang include Sun Yat-sen University & Synopsys.

Extended Ant Colony Optimization Algorithm for Power Electronic Circuit Design

Abstract: Ant colony optimization (ACO) is typically used to search paths through graphs. The concept is based on simulating the behavior of ants in finding paths from the colony to food. Its searching mechanism is applicable for optimizing electric circuits with components, like resistors and capacitors, available in discrete values. However, power electronic circuits (PECs) generally consist of components, like inductors, manufactured in continuous values. Therefore, the traditional ACO algorithm cannot be applied directly. In this paper, an extended ACO (eACO) that can search the optimal values of components manufactured in discrete and continuous values is presented. The idea is based on using the orthogonal design method (ODM) to dynamically update the database of the components available with continuous values, so that these components will have pseudo-discrete values in the search space. To speed up the optimization process, the ODM performs local search of the best combination around the best ant. The eACO also takes the component tolerances into account in evaluating the fitness value of each ant. The proposed algorithm has been successfully used to optimize the design of a buck regulator. The predicted results have been compared with the published results available in the literature and verified with experimental measurements.

Ripple: an effective routability-driven placer by iterative cell movement

Abstract: In this paper, we describe a routability-driven placer called Ripple. Two major techniques called cell inflation and net-based movement are used in global placement followed by a rough legalization step to reduce congestion. Cell inflation is performed in the horizontal and the vertical directions alternatively. We propose a new method called net-based movement, in which a target position is calculated for each cell by considering the movement of a net as a whole instead of working on each cell individually. In detailed placement, we use a combination of two kinds of strategy: the traditional HPWL-driven approach and our new congestion-driven approach. Experimental results show that Ripple is very effective in improving routability. Comparing with our pervious placer, which is the winner in the ISPD 2011 Contest, Ripple can further improve the overflow by 38% while reduce the runtime is reduced by 54%.

Ripple 2.0: high quality routability-driven placement via global router integration

Abstract: Due to a significant mismatch between the objectives of wirelength and routing congestion, the routability issue is becoming more and more important in VLSI design. In this paper, we present a high quality placer Ripple 2.0 to solve the routability-driven placement problem. We will study how to make use of the routing path information in cell spreading and relieve congestion with tangled logic in detail. Several techniques are proposed, including (1) lookahead routing analysis with pin density consideration, (2) routing path-based cell inflation and spreading and (3) robust optimization on congested cluster. With the official evaluation protocol, Ripple 2.0 outperforms the top contestants on the ICCAD 2012 Contest benchmark suite.

Local clock skew minimization using blockage-aware mixed tree-mesh clock network

Abstract: Clock network construction is one key problem in high performance VLSI design. Reducing the clock skew variation is one of the most important objectives during clock network synthesis. Local clock skew (LCS) is the clock skew between any two sinks with distance less than or equal to a given threshold. It is defined in the ISPD 2010 High Performance Clock Network Synthesis Contest [1], and it is a novel criterion that captures process variation effects on a clock network. In this paper, we propose a hybrid method that creates a mesh upon a tree topology. Total wire and buffer capacitance is minimized under the LCS and slew constraints. In our method, a clock mesh will be built first according to the positions and capacitance of the sinks. A top-level tree is then built to drive the mesh. A blockage-aware routing method is used during the tree construction. Experimental results show our efficiency and the solution generated by our approach can satisfy the LCS constraint of all the benchmarks in the contest [1], with a fair capacitance usage.

Obstacle-avoiding rectilinear Steiner minimum tree construction: an optimal approach

Abstract: In this paper, we present an efficient method to solve the obstacle-avoiding rectilinear Steiner minimum tree (OARSMT) problem optimally. Our work is a major improvement over the work proposed in [1]. First, a new kind of full Steiner trees (FSTs) called obstacle-avoiding full Steiner trees (OAFSTs) is proposed. We show that for any OARSMT problem there exists an optimal tree composed of OAFSTs only. We then extend the proofs on the possible topologies of FSTs in [2] to find the possible topologies of OAFSTs, showing that OAFSTs can be constructed easily. A two-phase algorithm for the construction of OARSMTs is then developed. In the first phase, a sufficient number of OAFSTs are generated. In the second phase, the OAFSTs are used to construct an OARSMT. Experimental results on several benchmarks show that the proposed method achieves 185 times speedup on average and is able to solve more benchmarks than the approach in [1].

Fundamental Concepts In The Design Of Experiments

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Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties

Abstract: Some uncertainties, such as the uncertain output power of a plug-in electric vehicle (PEV) due to its stochastic charging and discharging schedule, that of a wind generation unit due to the stochastic wind speed, and that of a solar generating source due to the stochastic illumination intensity, volatile fuel prices, and future uncertain load growth could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distribution system planning. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal siting and sizing of DGs. First, a mathematical model of CCP is developed with the minimization of the DGs' investment cost, operating cost, maintenance cost, network loss cost, as well as the capacity adequacy cost as the objective, security limitations as constraints, and the siting and sizing of DGs as optimization variables. Then, a Monte Carlo simulation-embedded genetic-algorithm-based approach is employed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is used to verify the feasibility and effectiveness of the developed model and method, and the test results have demonstrated that the voltage profile and power-supply reliability for customers can be significantly improved and the network loss substantially reduced.