# Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling

arXiv: https://arxiv.org/abs/2008.03107 · HTML: https://mlsanity.com/paper/2008.03107/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering](https://mlsanity.com/paper/2212.04953/) (2022)
- [Enhancing Downstream Analysis in Genome Sequencing: Species Classification While Basecalling](https://mlsanity.com/paper/2504.07065/) (2025)

## Abstract (arXiv)

Nanopore genome sequencing is the key to enabling personalized medicine, global food security, and virus surveillance. The state-of-the-art base-callers adopt deep neural networks (DNNs) to translate electrical signals generated by nanopore sequencers to digital DNA symbols. A DNN-based base-caller consumes 44.5% of total execution time of a nanopore sequencing pipeline. However, it is difficult to quantize a base-caller and build a power-efficient processing-in-memory (PIM) to run the quantized base-caller. In this paper, we propose a novel algorithm/architecture co-designed PIM, Helix, to power-efficiently and accurately accelerate nanopore base-calling. From algorithm perspective, we present systematic error aware training to minimize the number of systematic errors in a quantized base-caller. From architecture perspective, we propose a low-power SOT-MRAM-based ADC array to process analog-to-digital conversion operations and improve power efficiency of prior DNN PIMs. Moreover, we revised a traditional NVM-based dot-product engine to accelerate CTC decoding operations, and create a SOT-MRAM binary comparator array to process read voting. Compared to state-of-the-art PIMs, Helix improves base-calling throughput by 6×, throughput per Watt by 11.9× and per mm2 by 7.5× without degrading base-calling accuracy.