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Something has to give: scaling combinatorial computing by biological agents exploring physical networks encoding

Falco C M J M van Delft1, Giulia Ipolitti2, Dan V Nicolau1,3

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Biological agents on chips offer a novel hardware-embedded method for solving complex combinatorial problems faster than traditional computers. Key challenges include scaling, fabrication, and energy efficiency for these network-based computing systems.

Keywords:
NP-complete problemsbio-computationcombinatorial problemshardware-embedded solutionsnetwork-based computationsubset sum problem

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Area of Science:

  • Biocomputing
  • Network-based computation
  • Combinatorial problem-solving

Background:

  • Electronic computers face limitations with sequential processing for complex combinatorial problems.
  • On-chip network-based computation utilizes biological agents as a novel hardware-embedded approach.
  • This method aims for faster solutions to combinatorial problems compared to sequential electronic systems.

Purpose of the Study:

  • To review the mathematical principles of network-based computation using biological agents.
  • To analyze various combinatorial problems, including NP-complete types.
  • To present current proof-of-principle developments and identify technological challenges.

Main Methods:

  • In-depth analysis of the subset sum problem as a case study.
  • Exploration of different computing agents and operational 'run modes' for network-based systems.
  • Identification of scaling-related challenges in chip fabrication, readout reliability, and energy efficiency.

Main Results:

  • Network-based systems require visiting 2^C solutions for an input size C, necessitating distributed workload management.
  • Key technological hurdles identified include chip fabrication, reliable readout, and energy efficiency.
  • Comparison of estimated computing times for biological agents versus electronic computers.

Conclusions:

  • Network-based computation with biological agents presents a promising avenue for solving combinatorial problems.
  • Future developments like 'on the fly' agent labeling and travel history readout could significantly enhance performance.
  • Hardware-embedded solutions for complex computational challenges are achievable through this approach.