Computer Science > Hardware Architecture
[Submitted on 19 Nov 2024]
Title:Travel Time Based Task Mapping for NoC-Based DNN Accelerator
View PDF HTML (experimental)Abstract:Network-on-Chip (NoC) based architectures are recently proposed to accelerate deep neural networks in specialized hardware. Given that the hardware configuration is fixed post-manufacture, proper task mapping attracts researchers' interest. We propose a travel time-based task mapping method that allocates uneven counts of tasks across different Processing Elements (PEs). This approach utilizes the travel time recorded in the sampling window and implicitly makes use of static NoC architecture information and dynamic NoC congestion status. Furthermore, we examine the effectiveness of our method under various configurations, including different mapping iterations, flit sizes, and NoC architecture. Our method achieves up to 12.1% improvement compared with even mapping and static distance mapping for one layer. For a complete NN example, our method achieves 10.37% and 13.75% overall improvements to row-major mapping and distance-based mapping, respectively. While ideal travel time-based mapping (post-run) achieves 10.37% overall improvements to row-major mapping, we adopt a sampling window to efficiently map tasks during the running, achieving 8.17% (sampling window 10) improvement.
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.