Neural network execution using nicked DNA and microfluidics

TL;DR

This paper introduces a microfluidic-based integrated circuit that performs neural network computations directly on DNA data by enzymatically modifying DNA strands, enabling in-memory molecular computing without electrical conversion.

Contribution

It presents a novel DNA-based in-memory computing scheme using microfluidics and enzymes, advancing molecular neural network processing technology.

Findings

Demonstrates neural network computation performance on DNA data

Proposes a stochastic data representation scheme using DNA nicking

Details biochemical and microfluidic device design

Abstract

DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such as hard drives, solid-state storage, and optical media. However, computing on data stored in DNA is a largely unexplored challenge. This paper proposes an integrated circuit (IC) based on microfluidics that can perform complex operations such as artificial neural network (ANN) computation on data stored in DNA. It computes entirely in the molecular domain without converting data to electrical form, making it a form of in-memory computing on DNA. The computation is achieved by topologically modifying DNA strands through the use of enzymes called nickases. A novel scheme is proposed for representing data stochastically through the concentration of the DNA molecules that are nicked at specific sites. The paper provides details of the biochemical design, as well as the design, layout, and operation of the microfluidics device. Benchmarks are reported on the performance of neural network computation.