The Royal Society
Browse
rsfs20180034_si_001.docx (1.9 MB)

Supplementary Information from Something has to give: scaling combinatorial computing by biological agents exploring physical networks encoding NP-complete problems

Download (1.9 MB)
journal contribution
posted on 2018-10-08, 09:55 authored by Falco C. M. J. M. van Delft, Giulia Ipolitti, Dan V. Nicolau, Ayyappasamy Sudalaiyadum Perumal, Ondřej Kašpar, Sara Kheireddine, Sebastian Wachsmann-Hogiu
On-chip network-based computation, using biological agents, is a new hardware-embedded approach which attempts to find solutions to combinatorial problems, in principle, in a shorter time than the fast, but sequential electronic computers. This analytical review starts by describing the underlying mathematical principles, presents several types of combinatorial (including NP-complete) problems and shows current implementations of proof of principle developments. Taking the subset sum problem (SSP) as example for in-depth analysis, the review presents various options of computing agents, and compares several possible operation ‘run modes’ of network-based computer systems. Given the brute force approach of network-based systems for solving a problem of input size C, 2C solutions must be visited. As this exponentially increasing workload needs to be distributed in space, time, and per computing agent, this review identifies the scaling-related key technological challenges in terms of chip fabrication, readout reliability and energy efficiency. The estimated computing time of massively parallel or combinatorially operating biological agents is then compared to that of electronic computers. Among future developments which could considerably improve network-based computing, labelling agents ‘on the fly’ and the readout of their travel history at network exits, could offer promising avenues for finding hardware-embedded solutions to combinatorial problems.

History

Usage metrics

    Interface Focus

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC