用语义AI提升DNA存储，让物联网数据存得更准更稳
SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things
- 编码端加语义提取模块，精准保存信息含义
- 解码端用多拷贝过滤模型，信噪比提升2.61 dB
- 适合高可靠存储备份的物联网场景
随着物联网等技术的快速发展，全球数据量呈指数级增长，推动DNA存储成为未来云存储的潜在介质。本文提出一种语义人工智能增强型DNA存储（SemAI-DNA）范式，区别于主流深度学习方法，主要改进包括：1）在编码末端嵌入语义提取模块，实现对复杂语义信息的精细编码与存储；2）在解码末端设计前瞻性的多读取过滤模型，利用DNA分子固有的多拷贝特性提升系统容错能力，并优化解码器架构。数值结果表明，SemAI-DNA相较于传统深度学习方法，在峰值信噪比（PSNR）上提升2.61 dB，结构相似性指数（SSIM）提高0.13。
原文摘要 · Abstract (English)
In the wake of the swift evolution of technologies such as the Internet of Things (IoT), the global data landscape undergoes an exponential surge, propelling DNA storage into the spotlight as a prospective medium for contemporary cloud storage applications. This paper introduces a Semantic Artificial Intelligence-enhanced DNA storage (SemAI-DNA) paradigm, distinguishing itself from prevalent deep learning-based methodologies through two key modifications: 1) embedding a semantic extraction module at the encoding terminus, facilitating the meticulous encoding and storage of nuanced semantic information; 2) conceiving a forethoughtful multi-reads filtering model at the decoding terminus, leveraging the inherent multi-copy propensity of DNA molecules to bolster system fault tolerance, coupled with a strategically optimized decoder's architectural framework. Numerical results demonstrate the SemAI-DNA's efficacy, attaining 2.61 dB Peak Signal-to-Noise Ratio (PSNR) gain and 0.13 improvement in Structural Similarity Index (SSIM) over conventional deep learning-based approaches.
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