Neural network execution using nicked DNA and microfluidics
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, performing computations on the 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. We envision such a system to be suitable for highly dense, throughput-demanding bio-compatible applications such as an intelligent Organ-on-Chip or other biomedical applications that may not be latency-critical. 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 19, 2023
- Source ID
- 10.1371/journal.pone.0292228
Entities
People
- Amlan Ganguly
- Arnav Solanki
- Caiden Merritt
- Karisha Pradhan
- Marc D. Riedel
- Purab Ranjan Sutradhar
- Zak Griffin
Organizations
- National Science Foundation Directorate of Computer and Information Science and Engineering