hegongshan/Storage-for-AI-Paper
Accelerating AI Training and Inference from Storage Perspective (Must-read Papers on Storage for AI)
This project offers a curated collection of research papers focused on optimizing storage systems for AI and deep learning workloads. It helps AI/ML engineers and researchers understand how different storage formats, systems, caching, and data preprocessing techniques impact the speed and efficiency of training and inference. You'll find papers addressing common bottlenecks and solutions in handling large datasets for AI.
Use this if you are an AI/ML engineer or researcher looking for academic literature and practical solutions to improve the performance of your AI models by addressing data storage and loading inefficiencies.
Not ideal if you are looking for an out-of-the-box software tool or library to directly implement, as this is a collection of research papers rather than a deployable solution.
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Mar 10, 2026
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