Exact solution of the 2-dimensional grid arrangement problem

Oswald, Marcus; Reinelt, Gerhard; Wiesberg, Stefan

doi:10.1016/j.disopt.2012.07.002

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…• For architectures which do not support MOVE and use one physical location per qubit, we can extend MinLA to the 2-dimensional grid arrangement problem [27]. New methods are required to insert intra-set SWAPs and to determine initial qubit locations.…”

Section: Discussionmentioning

confidence: 99%

Optimization of quantum circuits for interaction distance in linear nearest neighbor architectures

Shafaei

Saeedi

Pedram

2013

Proceedings of the 50th Annual Design Automation Conference

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Optimization of the interaction distance between qubits to map a quantum circuit into one-dimensional quantum architectures is addressed. The problem is formulated as the Minimum Linear Arrangement (MinLA) problem. To achieve this, an interaction graph is constructed for a given circuit, and multiple instances of the MinLA problem for selected subcircuits of the initial circuit are formulated and solved. In addition, a lookahead technique is applied to improve the cost of the proposed solution which examines different subcircuit candidates. Experiments on quantum circuits for quantum Fourier transform and reversible benchmarks show the effectiveness of the approach.

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Section: Discussionmentioning

confidence: 99%

Optimization of quantum circuits for interaction distance in linear nearest neighbor architectures

Shafaei

Saeedi

Pedram

2013

Proceedings of the 50th Annual Design Automation Conference

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“…The current PathWave version uses a new, up to 10 3 times faster branch-and-cut algorithm to embed metabolic and signaling pathways into compact 2D lattice grids, on which wavelet transforms can be applied, and to allow for optimal arrangements of larger instances [ 12 ]. The new method to optimally arrange every KEGG/BiGG pathway G = ( V , E ) (represented by an undirected, unweighted network or graph G with nodes V and edges E ) into a compact 2D lattice grid computes an embedding of G into the lattice grid with the minimum possible total Manhattan distance (edge length) of nodes (metabolic reactions or signaling proteins) that are adjacent in G .…”

Section: Methodsmentioning

confidence: 99%

Network topology-based detection of differential gene regulation and regulatory switches in cell metabolism and signaling

et al. 2014

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BackgroundCommon approaches to pathway analysis treat pathways merely as lists of genes disregarding their topological structures, that is, ignoring the genes' interactions on which a pathway's cellular function depends. In contrast, PathWave has been developed for the analysis of high-throughput gene expression data that explicitly takes the topology of networks into account to identify both global dysregulation of and localized (switch-like) regulatory shifts within metabolic and signaling pathways. For this purpose, it applies adjusted wavelet transforms on optimized 2D grid representations of curated pathway maps.ResultsHere, we present the new version of PathWave with several substantial improvements including a new method for optimally mapping pathway networks unto compact 2D lattice grids, a more flexible and user-friendly interface, and pre-arranged 2D grid representations. These pathway representations are assembled for several species now comprising H. sapiens, M. musculus, D. melanogaster, D. rerio, C. elegans, and E. coli. We show that PathWave is more sensitive than common approaches and apply it to RNA-seq expression data, identifying crucial metabolic pathways in lung adenocarcinoma, as well as microarray expression data, identifying pathways involved in longevity of Drosophila.ConclusionsPathWave is a generic method for pathway analysis complementing established tools like GSEA, and the update comprises efficient new features. In contrast to the tested commonly applied approaches which do not take network topology into account, PathWave enables identifying pathways that are either known be involved in or very likely associated with such diverse conditions as human lung cancer or aging of D. melanogaster. The PathWave R package is freely available at http://www.ichip.de/software/pathwave.html.

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“…The mapping problem decides the topological placement of a selected set of router cores onto the PEs to achieve optimized interest metrics [11]. These problems are known as combinatorial optimization or discrete optimization problems, and are related to the minimum linear/quadratic assignment problem (QAP) [18]. Mathematically, this means mapping x IPs onto y network nodes such that x ≤ y; this has y!…”

Section: Background and Motivationmentioning

confidence: 99%

Energy consumption and performance comparison of DE optimization and PSO-based IP-core mapping technique for 2D and 3D network-on-chip

Jayshree¹,

Seetharaman²,

Pati³

2021

Semicond. Sci. Technol.

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This work presents an intellectual property mapping challenge for networks-on-chips, namely, optimizing communications costs (CC), energy consumption (EC), and performance. To address these problems, we propose meta-heuristic two-dimensional (2D) particle swarm optimization (PSO) and three-dimensional (3D) differential evolution (DE)-based mapping techniques. The proposed mapping techniques were analyzed and compared with the various mapping techniques using various real-time benchmarks. For a better comparison of the 3D DE-based mappings, the 2D-PSO and 2D genetic algorithm (GA) approaches were then extended to 3D-PSO and 3D-GA based mapping techniques. In this work, apart from the mapping effect, the routing impact has also been examined. The Xilinx Vivado IDE 16.2 was used for synthesis, and the ZYNQ 7000 FPGA was used for the implementation of the architecture. A network simulator was used for modeling and simulation. The results obtained show that the proposed 3D DE-based mapping has high performance, low CC, and low EC.

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Exact solution of the 2-dimensional grid arrangement problem

Cited by 6 publications

References 7 publications

Optimization of quantum circuits for interaction distance in linear nearest neighbor architectures

Optimization of quantum circuits for interaction distance in linear nearest neighbor architectures

Network topology-based detection of differential gene regulation and regulatory switches in cell metabolism and signaling

Energy consumption and performance comparison of DE optimization and PSO-based IP-core mapping technique for 2D and 3D network-on-chip

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