The Experts below are selected from a list of 28266 Experts worldwide ranked by ideXlab platform

Yaowen Chang - One of the best experts on this subject based on the ideXlab platform.

  • hamiltonian path based mixed cell height legalization for neighbor diffusion effect mitigation
    Design Automation Conference, 2020
    Co-Authors: Jianli Chen, Ziran Zhu, Qinghai Liu, Yimin Zhang, Wenxing Zhu, Yaowen Chang
    Abstract:

    In modern circuit designs, standard cells are designed with different heights based on the power, area, and other characteristics to address various design requirements. For those cells with different heights, in particular, there are inter-cell diffusion steps if the diffusion heights of neighboring cells are different, called the neighbor diffusion effect (NDE) which has become critical in advanced technology nodes. In this paper, we present a Hamiltonian-path-based mixed-cell-height legalization algorithm for NDE mitigation. We first present a row assignment method considering both cell disPlacements and diffusion steps to assign cells to their desired rows that meet the power-rail alignment constraints. Then, we propose a Hamiltonian-path-based diffusion-step reduction method to effectively reduce the NDE violations while preserving the global Placement Solution. Particularly, we develop a 2-approximation algorithm to find a minimum weight Hamiltonian path connecting two vertices, and a 1.5-approximation algorithm to find a minimum weight Hamiltonian path with a specified end vertex. Finally, we present an NDE-aware legalization method with design compaction to resolve overlaps and NDE violations. Experimental results show that our algorithm can resolve all NDE violations without any area overhead in reasonable runtime.

  • ntuplace a ratio partitioning based Placement algorithm for large scale mixed size designs
    International Symposium on Physical Design, 2005
    Co-Authors: Tungchieh Chen, Tienchang Hsu, Zhewei Jiang, Yaowen Chang
    Abstract:

    In this paper, we present a hierarchical ratio partitioning based Placement algorithm for large-scale mixed-size designs. The Placement algorithm consists of three steps: global Placement, legalization,and detailed Placement; it works in a hierarchical manner and integrates net-weighting partitioning, whitespace management, look-ahead bipartitioning, and fast legalization to handle the large-scale mixed-size Placement problems. Unlike the traditional partitioning-based technique that is based on balanced partitioning, we apply ratio partitioning in each level. Further, applying the look-ahead bipartitioning technique in each level, we can evaluate the feasibility of the Placement for sub-partitions more accurately. Therefore, we can find better ratios for the partitions, leading to easier legalization for the global Placement result and finally a better detailed Placement Solution. Experimental results show the efficiency and effectiveness of our algorithm.

Rick S Blum - One of the best experts on this subject based on the ideXlab platform.

  • Sensor Placement for Outage Identifiability in Power Distribution Networks
    IEEE Transactions on Smart Grid, 2020
    Co-Authors: Ananth Narayan Samudrala, Soummya Kar, M. Hadi Amini, Rick S Blum
    Abstract:

    Accurate topology information is critical for effective operation of power distribution networks. Line outages change the operational topology of a distribution network. Hence, outage detection is an important task. Power distribution networks are operated as radial trees and are recently adopting the integration of advanced sensors to monitor the network in real time. In this paper, a dynamic-programming-based minimum cost sensor Placement Solution is proposed for outage identifiability. A novel formulation of the sensor Placement as a cost optimization problem involving binary Placement decisions is proposed, along with a dynamic programming algorithm to solve it in polynomial time. Our sensor Placement Solution provides a set of locations in a distribution network where sensor Placement is mandatory for outage identifiability. We also extend our sensor Placement optimization problem and dynamic programming algorithm to the case in which the number of measurement channels of sensors are limited. The advantage of Placement strategies are that they incorporate various types of sensors, have a polynomial execution time, are cost effective and ensure outage identifiability for any arbitrary values of loads. Numerical results illustrating the proposed sensor Placement Solutions are presented under different scenarios for multiple feeder models including standard IEEE test feeders.

  • Sensor Placement for Outage Identifiability in Power Distribution Networks
    arXiv: Systems and Control, 2019
    Co-Authors: Ananth Narayan Samudrala, Soummya Kar, M. Hadi Amini, Rick S Blum
    Abstract:

    Accurate topology information is critical for effective operation of power distribution networks. Line outages change the operational topology of a distribution network. Hence, outage detection is an important task. Power distribution networks are operated as radial trees and are recently adopting the integration of advanced sensors to monitor the network in real time. In this paper, a dynamic-programming-based minimum cost sensor Placement Solution is proposed for outage identifiability. We propose a novel formulation of the sensor Placement as a cost optimization problem involving binary Placement decisions, and then provide an algorithm based on dynamic programming to solve it in polynomial time. The advantage of the proposed Placement strategy is that it incorporates various types of sensors, is independent of time varying load statistics, has a polynomial execution time and is cost effective. Numerical results illustrating the proposed sensor Placement Solution are presented for multiple feeder models including standard IEEE test feeders.

  • optimal sensor Placement for topology identification in smart power grids
    arXiv: Systems and Control, 2019
    Co-Authors: Ananth Narayan Samudrala, Hadi M Amini, Soummya Kar, Rick S Blum
    Abstract:

    Accurate network topology information is critical for secure operation of smart power distribution systems. Line outages can change the operational topology of a distribution network. As a result, topology identification by detecting outages is an important task to avoid mismatch between the {topology that the operator believes is present and the actual topology}. Power distribution systems are operated as radial trees and are recently adopting the integration of sensors to monitor the network in real time. In this paper, an optimal sensor Placement Solution is proposed that enables outage detection through statistical tests based on sensor measurements. Using two types of sensors, node sensors and line sensors, we propose a novel formulation for the optimal sensor Placement as a cost optimization problem with binary decision variables, i.e., {to place or not place a sensor at each bus/line}. The advantage of the proposed Placement strategy for outage detection is that it incorporates various types of sensors, is independent of load forecast statistics and is cost effective. Numerical results illustrating the Placement Solution are presented.

Djamal Zeghlache - One of the best experts on this subject based on the ideXlab platform.

  • energy efficient algorithm for vnf Placement and chaining
    IEEE ACM International Symposium Cluster Cloud and Grid Computing, 2017
    Co-Authors: Oussama Soualah, Marouen Mechtri, Chaima Ghribi, Djamal Zeghlache
    Abstract:

    This paper addresses energy efficient VNF Placement and chaining over NFV enabled infrastructures. VNF Placement and chaining are formulated as a decision tree search to overcome this NP-Hard problem complexity. The proposed approach is an extension of the Monte Carlo Tree Search (MCTS) method to achieve energy savings using physical resource consolidation and sharing VNFs between multiple tenants. A real cloud testbed and extensive simulations are used to assess performance and ability to scale with problem size. Evaluation results show significant reduction in energy consumption of the proposed Placement Solution compared to related work. The polynomial complexity of our proposal is highlighted by the simulation results.

Hannu Tenhunen - One of the best experts on this subject based on the ideXlab platform.

  • energy aware dynamic vm consolidation in cloud data centers using ant colony system
    International Conference on Cloud Computing, 2014
    Co-Authors: Fahimeh Farahnakian, Adnan Ashraf, Pasi Liljeberg, Tapio Pahikkala, Juha Plosila, Ivan Porres, Hannu Tenhunen
    Abstract:

    As the scale of a cloud data center becomes larger and larger, the energy consumption of the data center also grows rapidly. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy by turning off unused Physical Machines (PMs) in data centers. In this paper, we present a distributed controller to perform dynamic VM consolidation to improve the resource utilizations of PMs and to reduce their energy consumption. Moreover, we use the ant colony system to find a near-optimal VM Placement Solution based on the specified objective function. Experimental results on the real workload traces from more than a thousand PlanetLab VMs show that the proposed approach reduces energy consumption and maintains required performance levels in a large-scale data center.

Zhang, Ying-jun Angela - One of the best experts on this subject based on the ideXlab platform.

  • Optimizing AI Service Placement and Resource Allocation in Mobile Edge Intelligence Systems
    2021
    Co-Authors: Lin Zehong, Bi Suzhi, Zhang, Ying-jun Angela
    Abstract:

    Leveraging recent advances on mobile edge computing (MEC), edge intelligence has emerged as a promising paradigm to support mobile artificial intelligent (AI) applications at the network edge. In this paper, we consider the AI service Placement problem in a multi-user MEC system, where the access point (AP) places the most up-to-date AI program at user devices to enable local computing/task execution at the user side. To fully utilize the stringent wireless spectrum and edge computing resources, the AP sends the AI service program to a user only when enabling local computing at the user yields a better system performance. We formulate a mixed-integer non-linear programming (MINLP) problem to minimize the total computation time and energy consumption of all users by jointly optimizing the service Placement (i.e., which users to receive the program) and resource allocation (on local CPU frequencies, uplink bandwidth, and edge CPU frequency). To tackle the MINLP problem, we derive analytical expressions to calculate the optimal resource allocation decisions with low complexity. This allows us to efficiently obtain the optimal service Placement Solution by search-based algorithms such as meta-heuristic or greedy search algorithms. To enhance the algorithm scalability in large-sized networks, we further propose an ADMM (alternating direction method of multipliers) based method to decompose the optimization problem into parallel tractable MINLP subproblems. The ADMM method eliminates the need of searching in a high-dimensional space for service Placement decisions and thus has a low computational complexity that grows linearly with the number of users. Simulation results show that the proposed algorithms perform extremely close to the optimum and significantly outperform the other representative benchmark algorithms.Comment: 31 pages, 10 figure

  • Optimizing AI Service Placement and Resource Allocation in Mobile Edge Intelligence Systems
    2021
    Co-Authors: Lin Zehong, Bi Suzhi, Zhang, Ying-jun Angela
    Abstract:

    Leveraging recent advances on mobile edge computing (MEC), edge intelligence has emerged as a promising paradigm to support mobile artificial intelligence (AI) applications at the network edge. In this paper, we consider the AI service Placement problem in a multi-user MEC system, where the access point (AP) places the most up-to-date AI program at user devices to enable local computing/task execution at the user side. To fully utilize the stringent wireless spectrum and edge computing resources, the AP sends the AI service program to a user only when enabling local computing at the user yields a better system performance. We formulate a mixed-integer non-linear programming (MINLP) problem to minimize the total computation time and energy consumption of all users by jointly optimizing the service Placement (i.e., which users to receive the program) and resource allocation (on local CPU frequencies, uplink bandwidth, and edge CPU frequency). To tackle the MINLP problem, we derive analytical expressions to calculate the optimal resource allocation decisions with low complexity. This allows us to efficiently obtain the optimal service Placement Solution by search-based algorithms such as meta-heuristic or greedy search algorithms. To enhance the algorithm scalability in large-sized networks, we further propose an ADMM (alternating direction method of multipliers) based method to decompose the optimization problem into parallel tractable MINLP subproblems. The ADMM method eliminates the need of searching in a high-dimensional space for service Placement decisions and thus has a low computational complexity that grows linearly with the number of users. Simulation results show that the proposed algorithms perform extremely close to the optimum and significantly outperform the other representative benchmark algorithms.Comment: The paper has been accepted for publication by IEEE Transactions on Wireless Communications (May 2021