11 citations · 49 across the 19 of their papers we have counts for
7 papers · 1 filter
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models
Xinlin Li, Mariana Parazeres, Adam Oberman +3
With the advent of deep learning application on edge devices, researchers actively try to optimize their deployments on low-power and restricted memory devices. There are establish…
Training Integer-Only Deep Recurrent Neural Networks
Vahid Partovi Nia, Eyyüb Sari, Vanessa Courville +1
Recurrent neural networks (RNN) are the backbone of many text and speech applications. These architectures are typically made up of several computationally complex components such…
KronA: Parameter Efficient Tuning with Kronecker Adapter
Ali Edalati, Marzieh Tahaei, Ivan Kobyzev +3
Fine-tuning a Pre-trained Language Model (PLM) on a specific downstream task has been a well-known paradigm in Natural Language Processing. However, with the ever-growing size of P…
SeKron: A Decomposition Method Supporting Many Factorization Structures
Marawan Gamal Abdel Hameed, Ali Mosleh, Marzieh S. Tahaei +1
While convolutional neural networks (CNNs) have become the de facto standard for most image processing and computer vision applications, their deployment on edge devices remains ch…
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization
Xinlin Li, Bang Liu, Rui Heng Yang +3
Efficiently deploying deep neural networks on low-resource edge devices is challenging due to their ever-increasing resource requirements. To address this issue, researchers have p…
Is Integer Arithmetic Enough for Deep Learning Training?
Alireza Ghaffari, Marzieh S. Tahaei, Mohammadreza Tayaranian +2
The ever-increasing computational complexity of deep learning models makes their training and deployment difficult on various cloud and edge platforms. Replacing floating-point ari…