Literature review: Efficient deep neural networks techniques for medical image analysis
MA Abdou - Neural Computing and Applications, 2022 - Springer
Significant evolution in deep learning took place in 2010, when software developers started
using graphical processing units for general-purpose applications. From that date, the deep …
using graphical processing units for general-purpose applications. From that date, the deep …
An overview of efficient interconnection networks for deep neural network accelerators
Deep Neural Networks (DNNs) have shown significant advantages in many domains, such
as pattern recognition, prediction, and control optimization. The edge computing demand in …
as pattern recognition, prediction, and control optimization. The edge computing demand in …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
Deep learning for mobile multimedia: A survey
Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a
powerful instrument to automatically produce high-level abstractions of complex multimedia …
powerful instrument to automatically produce high-level abstractions of complex multimedia …
On-chip communication network for efficient training of deep convolutional networks on heterogeneous manycore systems
Convolutional Neural Networks (CNNs) have shown a great deal of success in diverse
application domains including computer vision, speech recognition, and natural language …
application domains including computer vision, speech recognition, and natural language …
Co-exploring neural architecture and network-on-chip design for real-time artificial intelligence
Hardware-aware Neural Architecture Search (NAS), which automatically finds an
architecture that works best on a given hardware design, has prevailed in response to the …
architecture that works best on a given hardware design, has prevailed in response to the …
Problems and challenges of emerging technology networks− on− chip: A review
AB Achballah, SB Othman, SB Saoud - Microprocessors and Microsystems, 2017 - Elsevier
Abstract Networks− on− chip (NoC) are an alternative to alleviate the problems of legacy
interconnect fabrics. However, many emerging technology NoC are developed and are now …
interconnect fabrics. However, many emerging technology NoC are developed and are now …
BiNoCHS: Bimodal network-on-chip for CPU-GPU heterogeneous systems
A Mirhosseini, M Sadrosadati, B Soltani… - Proceedings of the …, 2017 - dl.acm.org
CPU-GPU heterogeneous systems are emerging as architectures of choice for high-
performance energy-efficient computing. Designing on-chip interconnects for such systems …
performance energy-efficient computing. Designing on-chip interconnects for such systems …
Extending the power-efficiency and performance of photonic interconnects for heterogeneous multicores with machine learning
S Van Winkle, AK Kodi, R Bunescu… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
As communication energy exceeds computation energy in future technologies, traditional on-
chip electrical interconnects face fundamental challenges in the many-core era. Photonic …
chip electrical interconnects face fundamental challenges in the many-core era. Photonic …
Neuro-NoC: Energy optimization in heterogeneous many-core NoC using neural networks in dark silicon era
Due to the end of Dennard Scaling and the rise of dark silicon, it is essential to design
energy-efficient heterogeneous NoC under critical power and thermal constraints. The …
energy-efficient heterogeneous NoC under critical power and thermal constraints. The …