Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145295
Title: Hardware-software collaborative thermal sensing in optical network-on-chip–based manycore systems
Authors: Li, Mengquan
Liu, Weichen
Guan, Nan
Xie, Yiyuan
Ye, Yaoyao
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Li, M., Liu, W., Guan, N., Xie, Y., & Ye, Y. (2019). Hardware-software collaborative thermal sensing in optical network-on-chip–based manycore systems. ACM Transactions on Embedded Computing Systems, 18(6), 118:1-118:24. doi:10.1145/3362099
Journal: ACM Transactions on Embedded Computing Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/145295
ISSN: 1539-9087
DOI: 10.1145/3362099
Rights: © 2019 Association for Computing Machinery (ACM). All rights reserved. This paper was published in ACM Transactions on Embedded Computing Systems and is made available with permission of Association for Computing Machinery (ACM).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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