Comparative Analysis of Power Efficiency in Heterogeneous CPU -GPU Processors

Kaur, Rupinder; Mohammadi, Farah

doi:10.1109/csce60160.2023.00129

2023 Congress in Computer Science, Computer Engineering, &Amp;amp; Applied Computing (CSCE) 2023

DOI: 10.1109/csce60160.2023.00129

|View full text |Cite

Comparative Analysis of Power Efficiency in Heterogeneous CPU -GPU Processors

Rupinder Kaur,

Farah Mohammadi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks

Kaur,

Asad,

Mohammadi

2024

Computers

View full text Add to dashboard Cite

This comprehensive review explores the advancements in processing-in-memory (PIM) techniques and chiplet-based architectures for deep neural networks (DNNs). It addresses the challenges of monolithic chip architectures and highlights the benefits of chiplet-based designs in terms of scalability and flexibility. This review emphasizes dataflow-awareness, communication optimization, and thermal considerations in PIM-enabled manycore architectures. It discusses tailored dataflow requirements for different machine learning workloads and presents a heterogeneous PIM system for energy-efficient neural network training. Additionally, it explores thermally efficient dataflow-aware monolithic 3D (M3D) NoC architectures for accelerating CNN inferencing. Overall, this review provides valuable insights into the development and evaluation of chiplet and PIM architectures, emphasizing improved performance, energy efficiency, and inference accuracy in deep learning applications.

show abstract

A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks

Kaur,

Asad,

Mohammadi

2024

Computers

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Comparative Analysis of Power Efficiency in Heterogeneous CPU -GPU Processors

Cited by 1 publication

References 11 publications

A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks

A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks

Contact Info

Product

Resources

About