Please use this identifier to cite or link to this item: https://doi.org/10.1145/2544375.2544392
Title: Energy-aware task mapping and scheduling for reliable embedded computing systems
Authors: Das, A.
Kumar, A. 
Veeravalli, B. 
Keywords: Energy consumption
Fault tolerance
Multimedia applications
Synchronous dataflow graphs
Task mapping and scheduling
Issue Date: 2014
Source: Das, A., Kumar, A., Veeravalli, B. (2014). Energy-aware task mapping and scheduling for reliable embedded computing systems. Transactions on Embedded Computing Systems 13 (2 SUPPL.) : -. ScholarBank@NUS Repository. https://doi.org/10.1145/2544375.2544392
Abstract: Task mapping and scheduling are critical in minimizing energy consumption while satisfying the performance requirement of applications enabled on heterogeneous multiprocessor systems. An area of growing concern for modern multiprocessor systems is the increase in the failure probability of one or more component processors. This is especially critical for applications where performance degradation (e.g., throughput) directly impacts the quality of service requirement. This article proposes a design-time (offline) multi-criterion optimization technique for application mapping on embedded multiprocessor systems to minimize energy consumption for all processor fault-scenarios. A scheduling technique is then proposed based on self-timed execution to minimize the schedule storage and construction overhead at runtime. Experiments conducted with synthetic and real applications from streaming and nonstreaming domains on heterogeneous MPSoCs demonstrate that the proposed technique minimizes energy consumption by 22% and design space exploration time by 100x, while satisfying the throughput requirement for all processor fault-scenarios. For scalable throughput applications, the proposed technique achieves 30% better throughput per unit energy, compared to the existing techniques. Additionally, the self-timed execution-based scheduling technique minimizes schedule construction time by 95% and storage overhead by 92%. © 2014 ACM.
Source Title: Transactions on Embedded Computing Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/82279
ISSN: 15399087
DOI: 10.1145/2544375.2544392
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