# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 2 DNA Data Storage Readout and Hardware Acceleration
### Clustering-Based Readout Method and Hardware Acceleration  
To ensure the reliability and storage density of DNA data storage, various encoding and decoding methods have been proposed. [PERSON] et al. [10] introduced an encoding scheme based on Reed-Solomon (RS) codes. To enhance reliability, their method employs XOR operations to mix input data with a pseudo-random sequence, preventing repetitive bit patterns (e.g., consecutive '0' or '1'). This approach reduces systematic errors in DNA synthesis and sequencing while lowering decoding complexity. During the conversion to base sequences, a rotating code is applied to eliminate homopolymers (e.g., \"AAAA\" or \"GGGG\"), minimizing sequencing errors. For data readout, a clustering-based approach is used to group erroneous sequencing reads into clusters corresponding to the original DNA sequences, facilitating large-scale sequencing data processing. Consensus sequences are then determined through majority voting to correct errors. However, the extensive similarity calculations in clustering and majority voting, combined with the high complexity of decoding long RS codes, impose substantial computational challenges.  
To mitigate these issues, [PERSON] et al. [12] further proposed a concatenated RS coding scheme, where individual sequences are indexed, the outer code handles complete DNA strand loss, and the inner code corrects single-base errors. This method reorders sequences using unique index sequences, achieving DNA molecule identification with significantly lower complexity than clustering-based approaches. However, large-scale RS coding, complex data reordering, and index checking introduce substantial computational  
Figure 1: Workflow of DNA data storage.  
overhead. Moreover, index checking through lookup table mapping requires robust algorithms and hardware support, hindering its large-scale application in oligonucleotide pools and limiting its scalability.  
To address the high computational complexity and prolonged simulation time in DNA storage verification, [PERSON] et al. [28; 29; 30] developed DNAsim, a simulation framework integrating FPGA-based heterogeneous acceleration to enhance large-scale DNA data storage simulations. This framework focuses on edit-distance computation, a computationally intensive step in clustering algorithms. A dedicated FPGA accelerator, implemented on the Xilinx VC707 platform, was deeply integrated with a software simulation environment, forming a hybrid heterogeneous system. DNAsim supports the entire workflow, from data encoding and DNA sequence mapping to index addition, noise modeling, and decoding. It also enables the analysis of common DNA storage errors, such as insertions, deletions, substitutions, PCR noise, and sequencing biases. Specifically, for edit-distance computation, a parallel FPGA-based accelerator with 32 processing units was designed, enabling simultaneous calculations across multiple DNA sequence pairs. High-speed data transmission is achieved via a custom PCIe interface, leading to an 11-fold speedup in edit-distance computation compared to a software-only implementation and 5.5 times acceleration in clustering simulation, significantly reducing simulation time.  
Using hardware to evaluate coding performance is a common approach [32]. In our study, we explore how heterogeneous computing can be leveraged to accelerate DNA data readout and develop a hardware-accelerated processing core. This accelerator takes sequencing data as the input and performs primer sequence identification, index checking, majority voting, deinterleaving, and decoding in an end-to-end hardware-accelerated manner. A software layer performs real-time control of the hardware, optimizing the readout performance of the DNA storage system.