SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things

TL;DR

A semantic artificial intelligence-enhanced DNA storage (SemAI-DNA) paradigm is introduced, distinguishing itself from prevalent deep learning (DL)-based methodologies through two key modifications: embedding a semantic extraction module at the encoding terminus, and conceiving a forethoughtful multireads filtering model at the decoding terminus.

Abstract

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.

Figures (12)

• Figure 1: The system model of DJSCC-DNA 2023Deep.
• Figure 2: The proposed SemAI-DNA model.
• Figure 3: The structure of the ISE component.
• Figure 4: An example of calculating $\mathbf{Q}$ and $\mathbf{q}$, where $k=14,v=8$.
• Figure 5: An example of the $K$-mers process with $K=3$.