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

TL;DR

SemAI-DNA integrates semantic extraction and multi-reads filtering to enhance DNA storage for IoT, achieving significant improvements in data fidelity and fault tolerance over existing deep learning methods.

Contribution

This paper introduces a novel SemAI-DNA paradigm that combines semantic encoding and multi-reads filtering, advancing DNA storage technology for IoT applications.

Findings

Achieved 2.61 dB PSNR gain over traditional methods.

Improved SSIM by 0.13, indicating better image quality.

Enhanced fault tolerance through multi-reads filtering.

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.