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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".