Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
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 …
A graph placement methodology for fast chip design
Chip floorplanning is the engineering task of designing the physical layout of a computer
chip. Despite five decades of research 1, chip floorplanning has defied automation, requiring …
chip. Despite five decades of research 1, chip floorplanning has defied automation, requiring …
Machine learning for electronic design automation: A survey
With the down-scaling of CMOS technology, the design complexity of very large-scale
integrated is increasing. Although the application of machine learning (ML) techniques in …
integrated is increasing. Although the application of machine learning (ML) techniques in …
Chipformer: Transferable chip placement via offline decision transformer
Placement is a critical step in modern chip design, aiming to determine the positions of
circuit modules on the chip canvas. Recent works have shown that reinforcement learning …
circuit modules on the chip canvas. Recent works have shown that reinforcement learning …
Maskplace: Fast chip placement via reinforced visual representation learning
Placement is an essential task in modern chip design, aiming at placing millions of circuit
modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of …
modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of …
On joint learning for solving placement and routing in chip design
For its advantage in GPU acceleration and less dependency on human experts, machine
learning has been an emerging tool for solving the placement and routing problems, as two …
learning has been an emerging tool for solving the placement and routing problems, as two …
MLCAD: A survey of research in machine learning for CAD keynote paper
Due to the increasing size of integrated circuits (ICs), their design and optimization phases
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …
A timing engine inspired graph neural network model for pre-routing slack prediction
Fast and accurate pre-routing timing prediction is essential for timing-driven placement since
repetitive routing and static timing analysis (STA) iterations are expensive and …
repetitive routing and static timing analysis (STA) iterations are expensive and …
[图书][B] VLSI physical design: from graph partitioning to timing closure
The electronic design automation (EDA) industry develops software to support engineers in
the creation of new integrated circuit (IC) designs. Due to the high complexity of modern …
the creation of new integrated circuit (IC) designs. Due to the high complexity of modern …