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 …
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 …
[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 …
A survey of machine learning for computer architecture and systems
It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
A comprehensive review of bio-inspired optimization algorithms including applications in microelectronics and nanophotonics
The application of artificial intelligence in everyday life is becoming all-pervasive and
unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired …
unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired …
High performance graph convolutional networks with applications in testability analysis
Applications of deep learning to electronic design automation (EDA) have recently begun to
emerge, although they have mainly been limited to processing of regular structured data …
emerge, although they have mainly been limited to processing of regular structured data …
Accelerating chip design with machine learning
B Khailany - Proceedings of the 2020 ACM/IEEE Workshop on …, 2020 - dl.acm.org
As Moore's law has provided an exponential increase in chip transistor density, the unique
features we can now include in large chips are no longer predominantly limited by area …
features we can now include in large chips are no longer predominantly limited by area …
Machine learning for optical fiber communication systems: An introduction and overview
Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …
data. When information is extracted from these data, reconfigurable network elements and …
A survey on machine learning accelerators and evolutionary hardware platforms
Advanced computing systems have long been enablers for breakthroughs in artificial
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …
Versatile multi-stage graph neural network for circuit representation
Due to the rapid growth in the scale of circuits and the desire for knowledge transfer from old
designs to new ones, deep learning technologies have been widely exploited in Electronic …
designs to new ones, deep learning technologies have been widely exploited in Electronic …