MixTrain: accelerating DNN training via input mixing.

ABSTRACT: Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. An important factor contributing to the long training times is the increasing dataset complexity required to reach state-of-the-art performance in real-world applications. To address this challenge, we explore the use of input mixing, where multiple inputs are combined into a single composite input with an associated composite label for training. The goal is for training on the mixed input to achieve a similar effect as training separately on each the constituent inputs that it represents. This results in a lower number of inputs (or mini-batches) to be processed in each epoch, proportionally reducing training time. We find that naive in

SUBMITTER: Krithivasan S 

PROVIDER: S-EPMC11443600 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature