Abstract

Artificial intelligence over images improves every aspect of modern human life and has shown great success across numerous applications. However, it is costly to perform image AI. Image AI pipelines need to move heavy image files over the network so that many applications can concurrently process the images with varying resource budgets and performance requirements. As a result, data movement dominates the end-to-end image AI cost. This work presents Frequency-Store, the first column-store for images. Our intuition is that images do not need to be consumed by image AI one whole image at a time. Instead, there are “components” of data within each image that can be consumed separately and thus also can be stored separately. This decomposition allows the sharing of data movement across image AI processing pipelines both for training and inference. Frequency-Store breaks images into columns and stores batches of images column by column rather than storing individual images file by file. It utilizes the inherent blocks and frequencies-based structure in image data and defines a novel column abstraction. Column-wise storage allows applications with various characteristics and resource demands to share data efficiently. Columns store data items with similar characteristics, allowing tight data representations and efficient compression. We show that Frequency-Store improves inference/training time by up to 11x and compression ratio by up to 2.2x compared to state-of-the-art image AI storage.