26 citations · 97 across the 29 of their papers we have counts for
6 papers · 1 filter
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…
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition
Marawan Gamal Abdel Hameed, Marzieh S. Tahaei, Ali Mosleh +1
Modern Convolutional Neural Network (CNN) architectures, despite their superiority in solving various problems, are generally too large to be deployed on resource constrained edge…
: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Xinlin Li, Bang Liu, Yaoliang Yu +3
Shift neural networks reduce computation complexity by removing expensive multiplication operations and quantizing continuous weights into low-bit discrete values, which are fast a…
Importance of Data Loading Pipeline in Training Deep Neural Networks
Mahdi Zolnouri, Xinlin Li, Vahid Partovi Nia
Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loading data takes a signifi…
Deep Demosaicing for Edge Implementation
Ramchalam Kinattinkara Ramakrishnan, Shangling Jui, Vahid Patrovi Nia
Most digital cameras use sensors coated with a Color Filter Array (CFA) to capture channel components at every pixel location, resulting in a mosaic image that does not contain pix…