7 papers
CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training
Rezaul Karim, Austin Wen, Wang Zongzuo +3
The rapid growth in the size of large language models has necessitated the partitioning of computational workloads across accelerators such as GPUs, TPUs, and NPUs. However, these…
Accelerating the Low-Rank Decomposed Models
Habib Hajimolahoseini, Walid Ahmed, Austin Wen +1
Tensor decomposition is a mathematically supported technique for data compression. It consists of applying some kind of a Low Rank Decomposition technique on the tensors or matrice…
Is 3D Convolution with 5D Tensors Really Necessary for Video Analysis?
Habib Hajimolahoseini, Walid Ahmed, Austin Wen +1
In this paper, we present a comprehensive study and propose several novel techniques for implementing 3D convolutional blocks using 2D and/or 1D convolutions with only 4D and/or 3D…
SkipViT: Speeding Up Vision Transformers with a Token-Level Skip Connection
Foozhan Ataiefard, Walid Ahmed, Habib Hajimolahoseini +7
Vision transformers are known to be more computationally and data-intensive than CNN models. These transformer models such as ViT, require all the input image tokens to learn the r…
GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
Farnoosh Javadi, Walid Ahmed, Habib Hajimolahoseini +7
Massive transformer-based models face several challenges, including slow and computationally intensive pre-training and over-parametrization. This paper addresses these challenges…
SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
Habib Hajimolahoseini, Omar Mohamed Awad, Walid Ahmed +8
In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of…
Speeding up Resnet Architecture with Layers Targeted Low Rank Decomposition
Walid Ahmed, Habib Hajimolahoseini, Austin Wen +1
Compression of a neural network can help in speeding up both the training and the inference of the network. In this research, we study applying compression using low rank decomposi…