# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 1 Introduction  
Traditional storage media are unable to meet the large-scale storage demands brought about by the rapid growth of global data [1, 2, 3]. Synthetic DNA has emerged as a promising archival storage medium due to its ultra-high storage density, long-term stability, and low energy and maintenance costs [4, 5, 6]. In large-scale parallel synthesis, the synthesis of DNA remains limited to relatively short sequences, typically less than 300 nt [7]. Therefore, most DNA storage systems employ a distributed strategy by fragmenting data into multiple sub-blocks, each encoded into separate DNA molecules [8, 9]. For example, storing 1 TB of data may require approximately 30 billion unique DNA strands. Efficient data retrieval from DNA storage relies on high-redundancy sequencing to ensure both accuracy and integrity. This process generates a large volume of sequencing reads through parallel outputs, posing substantial computational challenges that conventionalarchitectures struggle to address [10; 11; 12]. Moreover, traditional clustering and multiple sequence alignment (MSA) algorithms incur substantial computational costs in processing these massive sequencing reads [13; 14; 15]. To facilitate sequence identification, each DNA strand is assigned a unique index sequence, which is often encoded with error correction codes (ECCs) to enhance robustness against errors [16; 17; 18]. While ECC-based index decoding can alleviate complexity to some extent, the computational burden remains significant in large-scale storage scenarios. Therefore, achieving high-throughput and low-latency DNA data retrieval remains a critical challenge. Traditional CPU-based architectures are increasingly inadequate for handling the intensive computational demands of DNA data readout. Heterogeneous computing has emerged as a promising paradigm that integrates multiple accelerators, such as CPU, GPU, and FPGA [19; 20]. It overcomes the performance bottlenecks of single-processor architectures under high workloads [21; 22; 23]. Recently, several studies have explored the use of heterogeneous computing for accelerating local sequence alignment and MSA in DNA readout. For example, Smith-Waterman (SW) alignment, known for its high computational complexity, has been implemented on FPGA for hardware acceleration [24; 25]. A parallel processing model on CPU-GPU heterogeneous platforms has been proposed for MSA [26; 27]. To facilitate large-scale DNA storage validation, an FPGA-based accelerator for edit-distance computation, along with a heterogeneous architecture simulation platform for DNA storage (DNA storage simulator, DNAsim), has been developed [28; 29; 30]. However, existing work primarily focuses on accelerating individual tasks such as edit-distance computation and lacks an integrated hardware acceleration solution for the entire DNA readout pipeline. We propose a novel FPGA-based heterogeneous computing architecture to address the computational challenges in short-fragment DNA data readout. The architecture accelerated critical DNA data readout tasks through a modular pipelined design. Primer sequence identification and an index check were implemented using combinational logic and staged pipelines, which improved sequence identification and clustering. A parallel majority voting module was introduced, along with a ping-pong-structured deinterleaver. An efficient LDPC decoder was integrated to enhance error correction, and multi-module task-level pipelining further boosted the throughput. To address the issue of non-continuous index values after encoding, a memory-efficient lookup structure was designed to reduce RAM usage. Compared to software-only solutions, the proposed architecture achieved significantly higher DNA data readout performance. We evaluated the accelerated system on an Alveo U200 accelerator card. The experimental results showed that the proposed accelerated system achieved a speedup of two orders of magnitude under typical error rates (0.6%, 1%, and 1.5%).