Toward an Embedded SoC for Mobile DNA Sequencing Applications
The development of small DNA sequencers offers a transformative opportunity for portable genomics. However, current systems lack integrated bioinformatics computing and instead rely on bulky external systems. To address this limitation, we propose an embedded System-on-Chip (SoC) architecture aimed at mobile DNA sequencing applications, representing an initial step towards a fully integrated bioinformatics solution. The proposed heterogeneous SoC leverages the open-source RISC-V architecture and incorporates two specialized bioinformatics accelerators: one for deep learning-based basecalling and the other for sequence alignment. An FPGA-realized basecalling accelerator achieves a nearly 2000× speedup over a standalone RISC-V core and demonstrates 11.5× and 1.2× higher energy efficiency than x86 CPUs and high-end GPUs, respectively, while maintaining an accuracy rate of 83.7%. A separate accelerator optimized for sequence comparison, offers 538× and 357× performance-per-joule boost relative to x86 CPUs and an existing state-of-the-art accelerator, respectively, while far exceeding other platforms. A preliminary ASIC prototype operates at 250 MHz with a power consumption of 34 mW, demonstrating significant potential for the proposed approach. Overall, the proposed SoC architecture presents a compelling first step towards embedded bioinformatics hardware for mobile DNA sequencing.
Authors
- Karim Hammad (ORCID: https://orcid.org/0000-0002-2311-3230)
- Abel Beyene
- Zhongpan Wu
- Sebastian Magierowski (ORCID: https://orcid.org/0000-0001-6333-0769)
- Ebrahim Ghafar‐Zadeh (ORCID: https://orcid.org/0000-0002-7090-2304)
Institutions
- York University (CA)
- Arab Academy for Science, Technology, and Maritime Transport (EG)
Publication Details
- Journal
- IEEE Transactions on Computational Biology and Bioinformatics
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1109/tcbbio.2025.3602886
- Primary Topic
- Embedded Systems Design Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00