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

TL;DR

Helix is a co-designed algorithm and architecture PIM that significantly accelerates nanopore genome base-calling, improving throughput and power efficiency without sacrificing accuracy.

Contribution

It introduces error-aware training for quantized base-callers and a novel SOT-MRAM-based PIM architecture for efficient analog-to-digital conversion and decoding operations.

Findings

6x increase in base-calling throughput

11.9x improvement in throughput per Watt

7.5x enhancement in throughput per mm^2

Abstract

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 $6\times$, throughput per Watt by $11.9\times$ and per $mm^2$ by $7.5\times$ without degrading base-calling accuracy.