Uploaded August 2026 | Updated September 2026, 3 weeks ago
The growth of synthesis capacity and decreasing costs over recent years have enabled researchers to explore larger DNA-based molecular computing systems. In particular, it has become cheaper and easier to study molecular systems that compute directly on data-bearing DNA. However, most contemporary in-memory compute systems lack error correction and redundancy, which will become increasingly important as “data storage grade” DNA with lower fidelity but higher synthesis density becomes more prevalent. The work presents a DNA codec that allows for strand- and file-level error correction while enabling the generated sequences to meet specifications required for DNA computing and other applications.
Presented by
Chris Takahashi, University of Washington
This is a presentation from the 2026 Storage and Computing with DNA Conference.
· Learn More about the SNIA DNA Data Storage Alliance: snia.org/groups/snia-dna-technology-affiliate
· SNIA Educational Library: snia.org/library
· X: twitter.com/SNIA
· LinkedIn: linkedin.com/company/snia
The growth of synthesis capacity and decreasing costs over recent years have enabled researchers to explore larger DNA-based molecular computing systems. In particular, it has become cheaper and easier to study molecular systems that compute directly on data-bearing DNA. However, most contemporary in-memory compute systems lack error correction and redundancy, which will become increasingly important as “data storage grade” DNA with lower fidelity but higher synthesis density becomes more prevalent. The work presents a DNA codec that allows for strand- and file-level error correction while enabling the generated sequences to meet specifications required for DNA computing and other applications.
Presented by
Chris Takahashi, University of Washington
This is a presentation from the 2026 Storage and Computing with DNA Conference.
· Learn More about the SNIA DNA Data Storage Alliance: snia.org/groups/snia-dna-technology-affiliate
· SNIA Educational Library: snia.org/library
· X: twitter.com/SNIA
· LinkedIn: linkedin.com/company/snia
