Parallelized Linear Classification with Volumetric Chemical Perceptrons

TL;DR

This paper introduces a chemical implementation of linear classifiers using volumetric perceptrons, enabling highly parallel processing of datasets through chemical mixtures and robotic liquid handling, with promising scalability for complex neural networks.

Contribution

It presents a novel chemical encoding technique for parallel dataset processing and demonstrates successful digit classification using chemical perceptrons, bridging chemical systems and neural computation.

Findings

Successful chemical classification of handwritten digits

Quantitative comparison of experimental and predicted results

Scalability potential for multilayer neural networks

Abstract

In this work, we introduce a new type of linear classifier that is implemented in a chemical form. We propose a novel encoding technique which simultaneously represents multiple datasets in an array of microliter-scale chemical mixtures. Parallel computations on these datasets are performed as robotic liquid handling sequences, whose outputs are analyzed by high-performance liquid chromatography. As a proof of concept, we chemically encode several MNIST images of handwritten digits and demonstrate successful chemical-domain classification of the digits using volumetric perceptrons. We additionally quantify the performance of our method with a larger dataset of binary vectors and compare the experimental measurements against predicted results. Paired with appropriate chemical analysis tools, our approach can work on increasingly parallel datasets. We anticipate that related approaches will be scalable to multilayer neural networks and other more complex algorithms. Much like recent demonstrations of archival data storage in DNA, this work blurs the line between chemical and electrical information systems, and offers early insight into the computational efficiency and massive parallelism which may come with computing in chemical domains.