  DNA is a leading candidate as the next archival storage media due to its
density, durability and sustainability. To read (and write) data DNA storage
exploits technology that has been developed over decades to sequence naturally
occurring DNA in the life sciences. To achieve higher accuracy for previously
unseen, biological DNA, sequencing relies on extending and training deep
machine learning models known as basecallers. This growth in model complexity
requires substantial resources, both computational and data sets. It also
eliminates the possibility of a compact read head for DNA as a storage medium.
  We argue that we need to depart from blindly using sequencing models from the
life sciences for DNA data storage. The difference is striking: for life
science applications we have no control over the DNA, however, in the case of
DNA data storage, we control how it is written, as well as the particular write
head. More specifically, data-carrying DNA can be modulated and embedded with
alignment markers and error correcting codes to guarantee higher fidelity and
to carry out some of the work that the machine learning models perform.
  In this paper, we study accuracy trade-offs between deep model size and error
correcting codes. We show that, starting with a model size of 107MB, the
reduced accuracy from model compression can be compensated by using simple
error correcting codes in the DNA sequences. In our experiments, we show that a
substantial reduction in the size of the model does not incur an undue penalty
for the error correcting codes used, therefore paving the way for portable
data-carrying DNA read head. Crucially, we show that through the joint use of
model compression and error correcting codes, we achieve a higher read accuracy
than without compression and error correction codes.
