On partitioning vs. placement rent properties

Verplaetse, P.; Dambre, Joni; Stroobandt, Dirk; Campenhout, J. Van

doi:10.1145/368640.368665

Cited by 22 publications

(16 citation statements)

References 12 publications

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“…Rent's exponent is deeply related to the placement and is often used to derive placement models [19]. We model crosstalk effects by clustering the structural netlist based on Rent's exponent values.…”

Section: Q'mentioning

confidence: 99%

“…The clustering algorithm is thus iterated until difference in Rent's exponent value between any pair of clusters falls below a user defined threshold. Rent's exponent values are computed as in [19] for large clusters and by brute force for small clusters. We found that, as shown in 3, a generated cluster is quite accurate in providing an estimate of the local placement around each cell in the placement phase that follows.…”

Section: Q'mentioning

confidence: 99%

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A unified gate sizing formulation for optimizing soft error rate, cross-talk noise and power under process variations

Bhattacharya

Ranganathan

2009

2009 10th International Symposium on Quality of Electronic Design

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The trends in technology scaling have made nanometer designs highly susceptible to reliability threats like soft errors and crosstalk noise while uncertainty in process parameters have made the physical realization of devices and interconnects unpredictable. The limitations in manufacturing processes and the impact of environmental noise poses a major threat to the signal quality, and hence the realization of reliable, low-power, high performance designs with acceptable parametric yields is a challenging problem. Most noise analysis and prevention techniques reported in the literature target single noise sources. However, reliability issues like crosstalk noise and radiation induced soft errors are deeply inter-related. Further, manufacturing variations have decreased the efficacy of online detection and correction schemes used for traditional noise optimization. In this work, we have proposed a methodology for simultaneous optimization of soft error rate(SER), crosstalk noise and power of circuits with delay constraints under process variations. Soft errors are modeled using a novel first order analysis model while crosstalk noise is modeled at the logic level using clustering based on Rent's exponent. The proposed multi-metric gate sizing methodology have been formulated into a mathematical program and has been efficiently solved. Experimental results on ISCAS'85 benchmark circuits indicate significant improvements in SER, crosstalk noise and timing yield compared to the corresponding constrained optimization problems.

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Section: Q'mentioning

confidence: 99%

Section: Q'mentioning

confidence: 99%

A unified gate sizing formulation for optimizing soft error rate, cross-talk noise and power under process variations

Bhattacharya

Ranganathan

2009

2009 10th International Symposium on Quality of Electronic Design

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“…This shows up both in questions about the adequacy of recursive bisection in constructive placements [14] and in questions about the relationship between the pre-placement and post-placement Rent parameters [15], [16] [see (1)]. These relations help us provide, at least, an asymptotic answer.…”

Section: A Why Does This Matter?mentioning

confidence: 99%

“…From this, we see that given a MoT growth sequence , we can create an HSRA growth sequence (15) That is, the directly corresponding HSRA sequence includes a two before every growth factor in the MoT sequence. Thus our , (1)* MoT sequence yields our (2 1)* HSRA sequence, and our , (2 1)* MoT sequence yields our (2 2 2 1)* HSRA sequence.…”

Section: ) Matching Growth Rates ( 'S)mentioning

confidence: 99%

“…That is, while there may be difference in the layout-based partitions (e.g., [15], [16]), these should, at most, be placement shuffles to reduce the constant factors associated with tree overlap among tree levels and will not change the asymptotic growth rate. The MoT and ToM layout described above tell us how to take any Rent characterized design and lay it out with Manhattan channel width.…”

Section: B Implications 1) Unified Design Spacementioning

confidence: 99%

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Unifying mesh- and tree-based programmable interconnect

DeHon

2004

IEEE Trans. VLSI Syst.

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Abstract-We examine the traditional, symmetric, Manhattan mesh design for field-programmable gate-array (FPGA) routing along with tree-of-meshes (ToM) and mesh-of-trees (MoT) based designs. All three networks can provide general routing for limited bisection designs (Rent's Rule with 1) and allow locality exploitation. They differ in their detailed topology and use of hierarchy. We show that all three have the same asymptotic wiring requirements. We bound this tightly by providing constructive mappings between routes in one network and routes in another. For example, we show that a ( ) MoT design can be mapped to a (2 ) linear population ToM and introduce a corner turn scheme which will make it possible to perform the reverse mapping from any ( ) linear population ToM to a (2 ) MoT augmented with a particular set of corner turn switches. One consequence of this latter mapping is a multilayer layout strategy for -node, linear population ToM designs that requires only 2( ) two-dimensional area for any when given sufficient wiring layers. We further show upper and lower bounds for global mesh routes based on recursive bisection width and show these are within a constant factor of each other and within a constant factor of MoT and ToM layout area. In the process we identify the parameters and characteristics which make the networks different, making it clear there is a unified design continuum in which these networks are simply particular regions.Index Terms-Butterfly fat tree (BFT), fat pyramid, fat tree, field-programmable gate-array (FPGA), hierarchical, hierarchical synchronous reconfigurable array (HSRA), interconnect, Manhattan, mesh, mesh-of-trees (MoT), multilevel metallization, Rent's rule, tree-of-meshes (ToM).

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Disparate Connectivity for Structural and Functional Networks is Revealed When Physical Location of the Connected Nodes is Considered

et al. 2014

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Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical connections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 different brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from verylarge-scale integration circuits analyses, shows that functional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrangements for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal-ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organizations that can only be identified when the physical locations of the nodes are included in the analysis.

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On partitioning vs. placement rent properties

Cited by 22 publications

References 12 publications

A unified gate sizing formulation for optimizing soft error rate, cross-talk noise and power under process variations

A unified gate sizing formulation for optimizing soft error rate, cross-talk noise and power under process variations

Unifying mesh- and tree-based programmable interconnect

Disparate Connectivity for Structural and Functional Networks is Revealed When Physical Location of the Connected Nodes is Considered

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