Adaptive Programming of Unconventional Nano-Architectures

TL;DR

This paper introduces adaptive programming techniques for nanocircuits with unconventional architectures, using control voltages and learning algorithms to enable flexible, reconfigurable, and fault-resistant nanocomputers.

Contribution

It presents a novel approach to program nanocircuits by treating them as black boxes and employing adaptive control and learning algorithms.

Findings

Logic gates successfully implemented

Reconfiguration of nanodevices demonstrated

Devices exhibit fault resistance

Abstract

Novel assembly processes for nanocircuits could present compelling alternatives to the detailed design and placement currently used for computers. The resulting architectures however may not be programmable by standard means. In this paper, nanocomputers with unconventional architectures are programmed using adaptive methods. The internals of the device are treated as a "black box" and programming is achieved by manipulating "control voltages". Learning algorithms are used to set the controls. As examples, logic gates and simple arithmetic circuits are implemented. Additionally, similar methods allow for reconfiguration of the devices, and makes them resistant to certain kinds of faults.