Toward an Embedded SoC for Mobile DNA Sequencing Applications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".