  We developed a new base caller DeepNano-coral for nanopore sequencing, which
is optimized to run on the Coral Edge Tensor Processing Unit, a small
USB-attached hardware accelerator. To achieve this goal, we have designed new
versions of two key components used in convolutional neural networks for speech
recognition and base calling. In our components, we propose a new way of
factorization of a full convolution into smaller operations, which decreases
memory access operations, memory access being a bottleneck on this device.
DeepNano-coral achieves real-time base calling during sequencing with the
accuracy slightly better than the fast mode of the Guppy base caller and is
extremely energy efficient, using only 10W of power. Availability:
https://github.com/fmfi-compbio/coral-basecaller
