10.6084/m9.figshare.7177895.v1
Falco C. M. J. M. van Delft
Falco C. M. J. M.
van Delft
Giulia Ipolitti
Giulia
Ipolitti
Dan V. Nicolau
Dan V.
Nicolau
Ayyappasamy Sudalaiyadum Perumal
Ayyappasamy
Sudalaiyadum Perumal
Ondřej Kašpar
Ondřej
Kašpar
Sara Kheireddine
Sara
Kheireddine
Sebastian Wachsmann-Hogiu
Sebastian
Wachsmann-Hogiu
Supplementary Information from Something has to give: scaling combinatorial computing by biological agents exploring physical networks encoding NP-complete problems
The Royal Society
2018
network-based computation
bio-computation
combinatorial problems
NP-complete problems
hardware-embedded solutions
subset sum problem
2018-10-08 09:55:45
Journal contribution
https://rs.figshare.com/articles/journal_contribution/Supplementary_Information_from_Something_has_to_give_scaling_combinatorial_computing_by_biological_agents_exploring_physical_networks_encoding_NP-complete_problems/7177895
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, 2<sup>C</sup> 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.