Hardware-software collaborative thermal sensing in optical network-on-chip–based manycore systems

Continuous technology scaling in manycore systems leads to severe overheating issues. To guarantee system reliability, it is critical to accurately yet efficiently monitor runtime temperature distribution for effective chip thermal management. As an emerging communication architecture for new-genera...

Full description

Saved in:
Bibliographic Details
Main Authors: Li, Mengquan, Liu, Weichen, Guan, Nan, Xie, Yiyuan, Ye, Yaoyao
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10356/145295
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Continuous technology scaling in manycore systems leads to severe overheating issues. To guarantee system reliability, it is critical to accurately yet efficiently monitor runtime temperature distribution for effective chip thermal management. As an emerging communication architecture for new-generation manycore systems, optical network-on-chip (ONoC) satisfies the communication bandwidth and latency requirements with low power dissipation. Moreover, observation shows that it can be leveraged for runtime thermal sensing. In this article, we propose a brand-new on-chip thermal sensing approach for ONoC-based manycore systems by utilizing the intrinsic thermal sensitivity of optical devices and the inter-processor communications in ONoCs. It requires no extra hardware but utilizes existing optical devices in ONoCs and combines them with lightweight software computation in a hardware-software collaborative manner. The effectiveness of the our approach is validated both at the device level and the system level through professional photonic simulations. Evaluation results based on synthetic communication traces and realistic benchmarks show that our approach achieves an average temperature inaccuracy of only 0.6648 K compared to ground-truth values and is scalable to be applied for large-size ONoCs.