Computational SSD Framework for Unified Semantic Image Retrieval

Explore a 20-minute conference talk from the SNIA Compute+Memory+Storage Summit that delves into developing a standardized machine learning framework for semantic image retrieval within SSDs. Learn how to overcome the challenges posed by diverse analytics formats in deep learning approaches by implementing an advanced transformer model that converts various types of analytics into uniform embedding formats. Discover techniques for designing an embedded system that processes both analytics and user queries through SBERT transformer models, converting them into N-dimensional vectors for efficient storage and retrieval. Master the implementation of clustering techniques to create an intent-based interface that improves search accuracy and reduces false positives. Gain insights from Western Digital's Vishwas Saxena on building a unified framework that eliminates dependency on multiple database types and query languages, making image retrieval more efficient on low-compute storage devices.