NVIDIA Corporation
OPTIMIZATION AND UPDATE SYSTEM FOR DEEP LEARNING MODELS

Abstract:

Traditionally, a software application is developed, tested, and then published for use to end users. Any subsequent update made to the software application is generally in the form of a human programmed modification made to the code in the software application itself, and further only becomes usable once tested and published by developers and/or publishers, and installed by end users having the previous version of the software application. This typical software application lifecycle causes delays in not only generating improvements to software applications, but also to those improvements being made accessible to end users. To help avoid these delays and improve performance of software applications, deep learning models may be made accessible to the software applications for use in performing inferencing operations to generate inferenced data output for the software applications, which the software applications may then use as desired. These deep learning models can furthermore be improved independently of the software applications using manual and/or automated processes.