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Nanopore-Aware Embedded Detection for Mobile DNA Sequencing: A Viterbi-HMM Design Versus Deep Learning Approaches.

Karim Hammad^1,2, Zhongpan Wu², Ebrahim Ghafar-Zadeh²

¹Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, Egypt.

|September 26, 2025

Summary

We developed an energy-efficient hardware-software system for DNA sequencing basecalling using a Hidden Markov Model (HMM) on a RISC-V processor. This approach significantly reduces power consumption for mobile biosensing applications.

Keywords:

ARM DNA sequencing FPGA RISC-V Rocket Viterbi edge computing nanopore sensing

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Area of Science:

Biotechnology
Genomics
Embedded Systems Engineering

Background:

Nanopore DNA sequencing offers real-time diagnostics but faces energy challenges for mobile use.
Deep learning (DL) models for basecalling are accurate but power-intensive, limiting portable applications.

Purpose of the Study:

To propose an energy-efficient hardware-software framework for DNA sequence detection.
To evaluate a Viterbi-based Hidden Markov Model (HMM) detector on a custom RISC-V core against DL-based methods.

Main Methods:

Implemented a Viterbi-based HMM detector on a 64-bit RISC-V core and a Virtex-7 FPGA.
Evaluated energy efficiency and inference accuracy against commodity processors and state-of-the-art DL basecallers.

Main Results:

Achieved 6.5x, 5.5x, and 4.6x higher energy efficiency than HMMs on x86, ARM, and Rocket systems.
Demonstrated 15x and 2.4x energy efficiency superiority over DL detectors with competitive accuracy.

Conclusions:

Classical probabilistic algorithms, like HMMs, offer practical advantages when integrated with lightweight embedded processors.
The proposed framework is suitable for energy-constrained biosensing, enabling field-based genomic surveillance and point-of-care diagnostics.