Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage

DNA data storage has emerged as a promising alternative to traditional storage media due to its high density and durability. However, large-scale DNA storage systems generate massive sequencing reads, posing substantial computational complexity and latency challenges for data readout. Here, we propose a novel heterogeneous computing architecture based on a field-programmable gate array (FPGA) to accelerate DNA data readout. The software component, running on a general computing platform, manages data distribution and schedules acceleration kernels. Meanwhile, the hardware acceleration kernel is deployed on an Alveo U200 data center accelerator card, executing multiple logical computing units within modules and utilizing task-level pipeline structures between modules to handle sequencing reads step by step. This heterogeneous computing acceleration system enables the efficient execution of the entire readout process for DNA data storage. We benchmark the proposed system against a CPU-based software implementation under various error rates and coverages. The results indicate that under high-error, low-coverage conditions (error rate of 1.5% and coverage of 15×), the accelerator achieves a peak speedup of up to 373.1 times, enabling the readout of 59.4 MB of stored data in just 12.40 s. Overall, the accelerator delivers a speedup of two orders of magnitude. Our proposed heterogeneous computing acceleration strategy provides an efficient solution for large-scale DNA data readout.

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Publication Details

Journal
Applied Sciences
Published
2025-05-01
DOI
https://doi.org/10.3390/app15095050
Primary Topic
DNA and Biological Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage

Qi Ge, Tingting Qi, Rui Qin, Weigang Chen et al.
Applied Sciences
DNA and Biological Computing
article

Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage

Qi Ge, Tingting Qi, Rui Qin, Weigang Chen, Quan Guo, Meiling Ren, Xiaopeng Gou
article en

Abstract

DNA data storage has emerged as a promising alternative to traditional storage media due to its high density and durability. However, large-scale DNA storage systems generate massive sequencing reads, posing substantial computational complexity and latency challenges for data readout. Here, we propose a novel heterogeneous computing architecture based on a field-programmable gate array (FPGA) to accelerate DNA data readout. The software component, running on a general computing platform, manages data distribution and schedules acceleration kernels. Meanwhile, the hardware acceleration kernel is deployed on an Alveo U200 data center accelerator card, executing multiple logical computing units within modules and utilizing task-level pipeline structures between modules to handle sequencing reads step by step. This heterogeneous computing acceleration system enables the efficient execution of the entire readout process for DNA data storage. We benchmark the proposed system against a CPU-based software implementation under various error rates and coverages. The results indicate that under high-error, low-coverage conditions (error rate of 1.5% and coverage of 15×), the accelerator achieves a peak speedup of up to 373.1 times, enabling the readout of 59.4 MB of stored data in just 12.40 s. Overall, the accelerator delivers a speedup of two orders of magnitude. Our proposed heterogeneous computing acceleration strategy provides an efficient solution for large-scale DNA data readout.

Applied SciencesVol. 15(9)
Tianjin University (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 92%
DNA and Biological Computing
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Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage — Qi Ge, Tingting Qi, et al. · Applied Sciences (2025) | TGRS Research Map | TGRS