A pareto-algebraic framework for signal power optimization in global routing

Shojaei, Hamid; Wu, Tai-Hsuan; Davoodi, Azadeh; Basten, Twan

doi:10.1145/1840845.1840935

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…The main algorithm is a guided local search that uses a penalization strategy. In Mansi et al [31] and Shojaei et al [32], the authors summarize the state of the art of the best approaches proposed for the MMKP. Mansi et al [31] investigate an iterative algorithm to find an upper and lower bounds for the optimal solution of the MMKP using different family of cuts.…”

Section: Related Workmentioning

confidence: 99%

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Htiouech¹,

Alzaidi²

2017

IJCA

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In this paper, we propose a multi-agent approach for solving the multidimensional multi-choice knapsack problem (called MMKP). The MMKP is an NP-Hard optimization problem in strong sense. It is considered as a combination of two other variants such as: the multi-choice knapsack problem (MCKP) and the multidimensional knapsack problem (MDKP). The MMKP can be applied in many problems in real world. It can model many industrial situations, such as capital budgeting, model of allocation resources and finance. The particular properties of the MMKP favor its decomposition into many MMKP sub-problems with small sizes. The assignment of sub-problems and the sharing of available resources are allocated to a first agent. Each subproblem is then solved by an agent. To work collaboratively, a strategic negotiation between agents has been defined. A coordinator agent (CA) will evaluate and merge the generated solutions to build a feasible solution to the initial problem. The choice rules of the CA is modeled as a multidimensional knapsack problem (MKP). The proposed method is able to solve several instances of literature effectively, in particular for large size instances. General TermsMetaheuristic, knapsack problem

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Section: Related Workmentioning

confidence: 99%

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Htiouech¹,

Alzaidi²

2017

IJCA

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“…Global routing problem variants can be modeled as MMKP instances [Shojaei et al 2010]. To illustrate scalability of CPH, we discuss collaborative global routing in which wirelength is minimized by reusing good routes found by different existing routing tools.…”

Section: Global Routingmentioning

confidence: 99%

A fast and scalable multidimensional multiple-choice knapsack heuristic

Shojaei

Basten

Geilen

et al. 2013

ACM Trans. Des. Autom. Electron. Syst.

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Many combinatorial optimization problems in the embedded systems and design automation domains involve decision making in multi-dimensional spaces. The multi-dimensional multiple-choice knapsack problem (MMKP) is among the most challenging of the encountered optimization problems. MMKP problem instances appear for example in chip multiprocessor run-time resource management and in global routing of wiring in circuits. Chip multiprocessor resource management requires solving MMKP under real-time constraints, whereas global routing requires scalability of the solution approach to extremely large MMKP instances. This paper presents a novel MMKP heuristic, CPH (for Compositional Pareto-algebraic Heuristic), which is a parameterized compositional heuristic based on the principles of Pareto algebra. Compositionality allows incremental computation of solutions. The parameterization allows tuning of the heuristic to the problem at hand. These aspects make CPH a very versatile heuristic. When tuning CPH for computation time, MMKP instances can be solved in real time with better results than the fastest MMKP heuristic so far. When tuning CPH for solution quality, it finds several new solutions for standard benchmarks that are not found by any existing heuristic. CPH furthermore scales to extremely large problem instances. We illustrate and evaluate the use of CPH in both chip multiprocessor resource management and in global routing.

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“…Note, all the edges in the same metal layer will have the same capacity using this approach. We first review a power model for global routing (proposed by us in [26]). …”

Section: A Power and Wirelength Minimizationmentioning

confidence: 99%

“…Each one provides An extended abstract of this paper was published in the 2010 International Symposium on Low Power Electronics and Design (ISLPED) [26].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Multiobjective Global Routing

Shojaei

Davoodi

Basten

2013

IEEE Trans. VLSI Syst.

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Abstract-This work presents a collaborative procedure for multi-objective global routing. Our procedure takes as input multiple global routing solutions, which are generated independently (e.g., by one router that runs in different modes concurrently, or by different routers running in parallel). It then performs multi-objective optimization based on Pareto algebra and quickly generates multiple global routing solutions with a tradeoff between the considered objectives. The user can control the number of generated solutions and the degree of exploring the tradeoff between them by constraining the maximum allowable degradation in each objective. This work then considers the following three multi-objective case studies: minimization of interconnect power and wirelength, minimization of routing congestion and wirelength, and minimization of wirelength with respect to the (finite-capacity) routing resources. The maximum allowable degradation in wirelength is specified in all cases. Our multi-objective procedure runs in only a few minutes for each of the ISPD 2008 benchmarks, even for the unroutable ones, which imposes a tolerable overhead in the design flow. In our simulations, we demonstrate the effectiveness of our procedure using five modern academic global routers.

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A pareto-algebraic framework for signal power optimization in global routing

Cited by 8 publications

References 10 publications

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

A fast and scalable multidimensional multiple-choice knapsack heuristic

Collaborative Multiobjective Global Routing

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