Implicit Neural Multiple Description for DNA-based data storage
Trung Hieu Le Xavier Pic Jeremy Mateos Marc Antonini
DNA exhibits remarkable potential as a data storage solution due to its
impressive storage density and long-term stability, stemming from its inherent
biomolecular structure. However, developing this novel medium comes with its
own set of challenges, particularly in addressing errors arising from storage
and biological manipulations. These challenges are further conditioned by the
structural constraints of DNA sequences and cost considerations. In response to
these limitations, we have pioneered a novel compression scheme and a
cutting-edge Multiple Description Coding (MDC) technique utilizing neural
networks for DNA data storage. Our MDC method introduces an innovative approach
to encoding data into DNA, specifically designed to withstand errors
effectively. Notably, our new compression scheme overperforms classic image
compression methods for DNA-data storage. Furthermore, our approach exhibits
superiority over conventional MDC methods reliant on auto-encoders. Its
distinctive strengths lie in its ability to bypass the need for extensive model
training and its enhanced adaptability for fine-tuning redundancy levels.
Experimental results demonstrate that our solution competes favorably with the
latest DNA data storage methods in the field, offering superior compression
rates and robust noise resilience.