Interpretation of rent's rule for ultralarge-scale integrated circuit designs, with an application to wirelength distribution models

Lanzerotti, Mary; Fiorenza, G.; Rand, R.

doi:10.1109/tvlsi.2004.837990

Cited by 16 publications

(28 citation statements)

References 19 publications

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“…Differing from the above definition of the number of pins T , the original study by E. F. Rent summed all pins of basic elements in a partition instead of counting only those that connect to outside the partition (Lanzerotti et al 2004). This definition of T was used in Beiu et al (2009) to extract Rent parameters for the mammalian neocortex from the powerlaw scaling of white-to-grey matter volume.…”

Section: Rent's Rulementioning

confidence: 99%

“…This approach describes the relationship between the size of a network part and its connections to the remainder of the network, two measures that are strongly connected to geometrical properties (Bassett et al 2010;Hagen et al 1994). This relationship has been mainly used for pre-placement area prediction in integrated circuits (Stroobandt and Kurdahi 1998;Christie and Stroobandt 2000;Lanzerotti et al 2004).…”

Section: Introductionmentioning

confidence: 99%

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Developing structural constraints on connectivity for biologically embedded neural networks

Partzsch

Schüffny

2012

Biol Cybern

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In this article, we analyse under which conditions an abstract model of connectivity could actually be embedded geometrically in a mammalian brain. To this end, we adopt and extend a method from circuit design called Rent's Rule to the highly branching structure of cortical connections. Adding on recent approaches, we introduce the concept of a limiting Rent characteristic that captures the geometrical constraints of a cortical substrate on connectivity. We derive this limit for the mammalian neocortex, finding that it is independent of the species qualitatively as well as quantitatively. In consequence, this method can be used as a universal descriptor for the geometrical restrictions of cortical connectivity. We investigate two widely used generic network models: uniform random and localized connectivity, and show how they are constrained by the limiting Rent characteristic. Finally, we discuss consequences of these restrictions on the development of cortex-size models.

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Section: Rent's Rulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing structural constraints on connectivity for biologically embedded neural networks

Partzsch

Schüffny

2012

Biol Cybern

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“…• P T SV,place : There are usually whitespaces in ICs [9] thus we can use those for TSV insertion. Some whitespaces, however, cannot be used because they are used for decap and minimization of congestion, the whitespaces are too small for TSV insertion, or they are far away from appropriate TSV locations.…”

Section: New 3d Design Parametersmentioning

confidence: 99%

Through-silicon-via aware interconnect prediction and optimization for 3D stacked ICs

Kim

Mukhopadhyay

Lim

2009

Proceedings of the 11th International Workshop on System Level Interconnect Prediction

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Individual dies in 3D integrated circuits are connected using throughsilicon-vias (TSVs). TSVs not only increase manufacturing cost, but also incur silicon area, delay, and power overhead. However, the effects of TSV overheads have not been studied thoroughly in the literature. In this paper, we analyze the impact of TSVs on silicon area and wirelength. We derive a new 3D wirelength distribution model considering TSV size. Based on this new prediction model, we explain the impact of several design parameters newly introduced in 3D ICs. We also present a case study to show how the model can help make early design decisions for 3D ICs.

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“…interpretation of Rent's two internal memoranda from 1960 [14] that introduced his methods (and graphical techniques) to illustrate the relationship between attributes of computer hardware components (for details see also [15] and [16]). …”

Section: Ngates(~nmentioning

confidence: 99%

“…we shall start from exploring the connectivity of the brain, and comparing it to known computer interconnect (network) topologies. For doing these we shall rely on Rent's rule [13], [14], and in particular on a fresh interpretation of Rent's rule reported in [15], [16]. Our aim is to identify those interconnect (network) topologies which could be a better fit for future brain-inspired nanoarchitectures, looking for hybrid (hierarchical) combination of two network topologies which would allow us to optimally approach (mimic) brain's connectivity.…”

Section: Introductionmentioning

confidence: 99%

On brain-inspired hybrid topologies for nano-architectures - a Rent’s rule approach -

Beiu

Madappuram

McGinnity

2008

2008 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation

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This paper will start by comparing brain's connectivity (based on different analyses of neurological data) versus well-known network topologies (originally used in massively parallel super-computers), in view of the latest interpretation of Rent's rule. These will reveal that the brain is in very good agreement with Rent's rule average growth rate. With respect to classical network topologies, the crossbar (only for quite small sizes) and the cube connected cycles (for a wider range) look like promising contenders (for the brain), while in fact any network topology falls short of properly mimicking brain's connectivity. That is why, we will go on exploring hybrid (hierarchical) combination of two network topologies, allowing us to identify those (hybrid network topologies) which could closely emulate brain's connectivity (as well as the particular ranges where this is happening).

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Interpretation of rent's rule for ultralarge-scale integrated circuit designs, with an application to wirelength distribution models

Cited by 16 publications

References 19 publications

Developing structural constraints on connectivity for biologically embedded neural networks

Developing structural constraints on connectivity for biologically embedded neural networks

Through-silicon-via aware interconnect prediction and optimization for 3D stacked ICs

On brain-inspired hybrid topologies for nano-architectures - a Rent’s rule approach -

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Interpretation of rent's rule for ultralarge-scale integrated circuit designs, with an application to wirelength distribution models

Cited by 16 publications

References 19 publications

Developing structural constraints on connectivity for biologically embedded neural networks

Developing structural constraints on connectivity for biologically embedded neural networks

Through-silicon-via aware interconnect prediction and optimization for 3D stacked ICs

On brain-inspired hybrid topologies for nano-architectures - a Rent&#x2019;s rule approach -

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On brain-inspired hybrid topologies for nano-architectures - a Rent’s rule approach -