Parallel computation with molecular-motor-propelled agents in nanofabricated networks
Parallel computation with molecular-motor-propelled agents in nanofabricated networks is a paper by Dan Nicolau Jr. et. al. in the Proceedings of the National Academy of Sciences (PNAS).
Abstract
The combinatorial nature of many important mathematical problems, including NP-completeness problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers.
There have been significant efforts in conceiving parallel computation approaches in the past, for example:
- DNA computation
- Quantum computation
- Microfluidics-based computation
These approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective.
Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device.
Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem.
This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation.
We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-completeness problem.
Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.
