Title: Parallelized Linear Classification with Volumetric Chemical Perceptrons,
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.