On two-layer brain-inspired hierarchical topologies – a rent's rule approach –

Authors:
Valeriu Beiu;Basheer A. M. Madappuram;Peter M. Kelly;Liam J. McDaid
Affiliations:
Faculty of Information Technology, Center for Neural Inspired Nano Architectures (CNINA), United Arab Emirates University, Al Ain, UAE;Faculty of Information Technology, Center for Neural Inspired Nano Architectures (CNINA), United Arab Emirates University, Al Ain, UAE;School of Intelligent Systems, University of Ulster, Magee, UK;School of Intelligent Systems, University of Ulster, Magee, UK
Venue:
Transactions on High-Performance Embedded Architectures and Compilers IV
Year:
2011

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Abstract

This research compares the brain's connectivity (based on different analyses of neurological data) with well-known network topologies (originally used in super-computers) using Rent's rule. The comparison reveals that brain connectivity is in good agreement with Rent's rule. However, the known network topologies fall short of being strong contenders for mimicking brain's connectivity. That is why we perform a detailed Rent-based (top-down) connectivity analysis of generic two-layer hierarchical network topologies. This analysis aims to identify generic two-layer hierarchical network topologies which could closely mimic brain's connectivity. The range of granularities (i.e., number of gates/cores/neurons) where such mimicking is possible are identified and discussed. These results should have implications for the design of future networks-on-chip in general, and for the burgeoning field of multi/many-core processors in particular (in the medium term), as well as for forward-looking investigations on emerging brain-inspired nano-architectures (in the long run).