TY - GEN
T1 - Proactive aging mitigation in CGRAs through utilization-aware allocation
AU - Brandalero, Marcelo
AU - Lignati, Bernardo Neuhaus
AU - Carlos Schneider Beck, Antonio
AU - Shafique, Muhammad
AU - Hubner, Michael
N1 - Funding Information:
This work is supported in part by the German Research Foundation (DFG) as part of the priority program ”Dependable Embedded Systems” (SPP1500 - http://spp1500.itec.kit.edu), and in part by the Postoc Network Brandenburg (https://www.postdoc-network-brandenburg.de/en/).
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - Resource balancing has been effectively used to mitigate the long-term aging effects of Negative Bias Temperature Instability (NBTI) in multi-core and Graphics Processing Unit (GPU) architectures. In this work, we investigate this strategy in Coarse-Grained Reconfigurable Arrays (CGRAs) with a novel application-to-CGRA allocation approach. By introducing important extensions to the reconfiguration logic and the datapath, we enable the dynamic movement of configurations throughout the fabric and allow overutilized Functional Units (FUs) to recover from stress-induced NBTI aging. Implementing the approach in a resource-constrained state-of-the-art CGRA reveals 2.2× lifetime improvement with negligible performance overheads and less than 10% increase in area.
AB - Resource balancing has been effectively used to mitigate the long-term aging effects of Negative Bias Temperature Instability (NBTI) in multi-core and Graphics Processing Unit (GPU) architectures. In this work, we investigate this strategy in Coarse-Grained Reconfigurable Arrays (CGRAs) with a novel application-to-CGRA allocation approach. By introducing important extensions to the reconfiguration logic and the datapath, we enable the dynamic movement of configurations throughout the fabric and allow overutilized Functional Units (FUs) to recover from stress-induced NBTI aging. Implementing the approach in a resource-constrained state-of-the-art CGRA reveals 2.2× lifetime improvement with negligible performance overheads and less than 10% increase in area.
KW - Aging
KW - Allocation
KW - Coarse-Grained Reconfigurable Arrays (CGRAs)
KW - Mapping
KW - Reconfigurable systems
KW - Utilization-aware
UR - http://www.scopus.com/inward/record.url?scp=85093930535&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85093930535&partnerID=8YFLogxK
U2 - 10.1109/DAC18072.2020.9218586
DO - 10.1109/DAC18072.2020.9218586
M3 - Conference contribution
AN - SCOPUS:85093930535
T3 - Proceedings - Design Automation Conference
BT - 2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 57th ACM/IEEE Design Automation Conference, DAC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -