# Nanopore Base Calling on the Edge

arXiv: https://arxiv.org/abs/2011.04312 · HTML: https://mlsanity.com/paper/2011.04312/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization](https://mlsanity.com/paper/2409.18850/) (2024)
- [TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering](https://mlsanity.com/paper/2212.04953/) (2022)
- [Dynamic Pooling Improves Nanopore Base Calling Accuracy](https://mlsanity.com/paper/2105.07520/) (2021)
- [DNA data storage, sequencing data-carrying DNA](https://mlsanity.com/paper/2205.05488/) (2022)
- [CRANE: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models](https://mlsanity.com/paper/2603.20420/) (2026)

## Abstract (arXiv)

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