2 citations · 4 across the 10 of their papers we have counts for
10 papers
ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training
Maryam Dialameh, Rezaul Karim, Hossein Rajabzadeh +5
This paper introduces ECHO-LLaMA, an efficient LLaMA architecture designed to improve both the training speed and inference throughput of LLaMA architectures while maintaining its…
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…
Single Parent Family: A Spectrum of Family Members from a Single Pre-Trained Foundation Model
Habib Hajimolahoseini, Mohammad Hassanpour, Foozhan Ataiefard +2
This paper introduces a novel method of Progressive Low Rank Decomposition (PLRD) tailored for the compression of large language models. Our approach leverages a pre-trained model,…
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…
Improving Resnet-9 Generalization Trained on Small Datasets
Omar Mohamed Awad, Habib Hajimolahoseini, Michael Lim +4
This paper presents our proposed approach that won the first prize at the ICLR competition on Hardware Aware Efficient Training. The challenge is to achieve the highest possible ac…
Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization
Habib Hajimolahoseini, Walid Ahmed, Yang Liu
Low Rank Decomposition (LRD) is a model compression technique applied to the weight tensors of deep learning models in order to reduce the number of trainable parameters and comput…