Frequency-Store: Scaling Image AI by A Column-Store for Images

Utku Sirin, Victoria Kauffman, Aadit Saluja, Florian Klein, Jeremy Hsu, Stratos Idreos

Abstract:

Artificial intelligence over images improves every aspect of modern
human life and has shown great success across numerous applica-
tions. 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 re-
source 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 characteris-
tics and resource demands to share data efficiently. Columns store
data items with similar characteristics, allowing tight data represen-
tations 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 stor