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 …
Deep configuration performance learning: A systematic survey and taxonomy
Performance is arguably the most crucial attribute that reflects the quality of a configurable
software system. However, given the increasing scale and complexity of modern software …
software system. However, given the increasing scale and complexity of modern software …
Fog in the clouds: UAVs to provide edge computing to IoT devices
G Faraci, C Grasso, G Schembra - ACM Transactions on Internet …, 2020 - dl.acm.org
Internet of Things (IoT) has emerged as a huge paradigm shift by connecting a versatile and
massive collection of smart objects to the Internet, coming to play an important role in our …
massive collection of smart objects to the Internet, coming to play an important role in our …
Dividable configuration performance learning
Machine/deep learning models have been widely adopted for predicting the configuration
performance of software systems. However, a crucial yet unaddressed challenge is how to …
performance of software systems. However, a crucial yet unaddressed challenge is how to …
Modeling and predicting the sensitivity of high-performance concrete compressive strength using machine learning methods
WH Al Yamani, DM Ghunimat, MM Bisharah - Asian Journal of Civil …, 2023 - Springer
Concrete compressive strength is the most important performance requirement in structural
engineering when developing both traditional concrete and high-performance concrete …
engineering when developing both traditional concrete and high-performance concrete …
A survey of machine learning applied to computer architecture design
DD Penney, L Chen - arXiv preprint arXiv:1909.12373, 2019 - arxiv.org
Machine learning has enabled significant benefits in diverse fields, but, with a few
exceptions, has had limited impact on computer architecture. Recent work, however, has …
exceptions, has had limited impact on computer architecture. Recent work, however, has …
The role of machine learning in scientific workflows
Machine learning (ML) is being applied in a number of everyday contexts from image
recognition, to natural language processing, to autonomous vehicles, to product …
recognition, to natural language processing, to autonomous vehicles, to product …
Predicting Software Performance with Divide-and-Learn
Predicting the performance of highly configurable software systems is the foundation for
performance testing and quality assurance. To that end, recent work has been relying on …
performance testing and quality assurance. To that end, recent work has been relying on …
Exploring the role of machine learning in scientific workflows: Opportunities and challenges
In this survey, we discuss the challenges of executing scientific workflows as well as existing
Machine Learning (ML) techniques to alleviate those challenges. We provide the context …
Machine Learning (ML) techniques to alleviate those challenges. We provide the context …
Composite-ISA cores: Enabling multi-ISA heterogeneity using a single ISA
A Venkat, H Basavaraj… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Heterogeneous multicore architectures are comprised of multiple cores of different sizes,
organizations, and capabilities. These architectures maximize both performance and energy …
organizations, and capabilities. These architectures maximize both performance and energy …