Implementing parallel arithmetic via acetylation and its application to chemical image processing

作者：Amanda Dombroski, Kady Oakley, Christopher E. Arcadia, Farnaz Nouraei, Shui Ling Chen, Christopher Rose, Brenda M. Rubenstein, Jacob K. Rosenstein, Sherief Reda, Eunsuk Kim · 发表于：Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 年份：2021 · DOI：10.1098/rspa.2020.0899 · 被引用次数：1 · 研究领域：Advanced Memory and Neural Computing、Machine Learning in Materials Science、Innovative Microfluidic and Catalytic Techniques Innovation

Chemical mixtures can be leveraged to store large amounts of data in a highly compact form and have the potential for massive scalability owing to the use of large-scale molecular libraries. With the parallelism that comes from having many species available, chemical-based memory can also provide the physical substrate for computation with increased throughput. Here, we represent non-binary matrices in chemical solutions and perform multiple matrix multiplications and additions, in parallel, using chemical reactions. As a case study, we demonstrate image processing, in which small greyscale images are encoded in chemical mixtures and kernel-based convolutions are performed using phenol acetylation reactions. In these experiments, we use the measured concentrations of reaction products (phenyl acetates) to reconstruct the output image. In addition, we establish the chemical criteria required to realize chemical image processing and validate reaction-based multiplication. Most importantly, this work shows that fundamental arithmetic operations can be reliably carried out with chemical reactions. Our approach could serve as a basis for developing more advanced chemical computing architectures.