A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers
Singh, G.; Alser, M.; Khodamoradi, A.; Denolf, K.; Firtina, C.; Cavlak, M. B.; Corporaal, H.; Mutlu, O.
Show abstract
Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.
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