Helix: Algorithm / architecture co-design for accelerating nanopore genome base-calling

Abstract

Nanopore genome sequencing is the key to enabling personalizedmedicine, global food security, and virus surveillance. The state-ofthe-art base-callers adopt deep neural networks (DNNs) to translate electrical signals generated by nanopore sequencers to digitalDNA 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-efficientprocessing-in-memory (PIM) to run the quantized base-caller. Although conventional network quantization techniques reduce thecomputing overhead of a base-caller by replacing floating-pointmultiply-accumulations by cheaper fixed-point operations, it significantly increases the number of systematic errors that cannotbe corrected by read votes. The power density of prior nonvolatilememory (NVM)-based PIMs has already exceeded memory thermaltolerance even with active heat sinks, because their power efficiencyis severely limited by analog-to-digital converters (ADC). Finally,Connectionist Temporal Classification (CTC) decoding and readvoting cost 53.7% of total execution time in a quantized base-caller,and thus became its new bottleneck.In this paper, we propose a novel algorithm/architecture codesigned PIM, Helix, to power-efficiently and accurately acceleratenanopore base-calling. From algorithm perspective, we present systematic error aware training to minimize the number of systematicerrors 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 powerefficiency of prior DNN PIMs. Moreover, we revised a traditionalNVM-based dot-product engine to accelerate CTC decoding operations, and create a SOT-MRAM binary comparator array to processread voting. Compared to state-of-the-art PIMs, Helix improvesbase-calling throughput by 6×, throughput per Watt by 11.9× andper mm2 by 7.5× without degrading base-calling accuracy.