TY - JOUR

T1 - Something has to give

T2 - Scaling combinatorial computing by biological agents exploring physical networks encoding NP-complete problems

AU - Van Delft, Falco C.M.J.M.

AU - Ipolitti, Giulia

AU - Nicolau, Dan V.

AU - Perumal, Ayyappasamy Sudalaiyadum

AU - Kašpar, Ondřej

AU - Kheireddine, Sara

AU - Wachsmann-Hogiu, Sebastian

AU - Nicolau, Dan V.

PY - 2018/12/6

Y1 - 2018/12/6

N2 - 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 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.

AB - 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 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.

KW - Bio-computation

KW - Combinatorial problems

KW - Hardware-embedded solutions

KW - Network-based computation

KW - NP-complete problems

KW - Subset sum problem

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U2 - 10.1098/rsfs.2018.0034

DO - 10.1098/rsfs.2018.0034

M3 - Review article

AN - SCOPUS:85056538474

VL - 8

JO - Interface Focus

JF - Interface Focus

SN - 2042-8898

IS - 6

ER -