Conference proceeding
Artificial Intelligence Based Task Mapping and Pipelined Scheduling for Checkpointing on Real Time Systems with Imperfect Fault Detection
PROCEEDINGS OF THE 2014 IEEE INTERNATIONAL SYMPOSIUM ON DEFECT AND FAULT TOLERANCE IN VLSI AND NANOTECHNOLOGY SYSTEMS (DFTS), pp 134-140
01 Jan 2014
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Fault-tolerance is emerging as one of the important optimization objectives for designs in deep submicron technology nodes. This paper proposes a technique of application mapping and scheduling with checkpointing on a multiprocessor system to maximize the reliability considering transient faults. The proposed model incorporates checkpoints with imperfect fault detection probability, and pipelined execution and cyclic dependency associated with multimedia applications. This is solved using an Artificial Intelligence technique known as Particle Swarm Optimization to determine the number of checkpoints of every task of the application that maximizes the confidence of the output. The proposed approach is validated experimentally with synthetic and real-life application graphs. Results demonstrate the proposed technique improves the probability of correct result by an average 15% with imperfect fault detection. Additionally, even with 100% fault detection, the proposed technique is able to achieve better results (25% higher confidence) as compared to the existing fault-tolerant techniques.
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Details
- Title
- Artificial Intelligence Based Task Mapping and Pipelined Scheduling for Checkpointing on Real Time Systems with Imperfect Fault Detection
- Creators
- Anup Das - National University of SingaporeAkash Kumar - National University of SingaporeBharadwaj Veeravalli - National University of SingaporeIEEE
- Publication Details
- PROCEEDINGS OF THE 2014 IEEE INTERNATIONAL SYMPOSIUM ON DEFECT AND FAULT TOLERANCE IN VLSI AND NANOTECHNOLOGY SYSTEMS (DFTS), pp 134-140
- Series
- IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems
- Publisher
- IEEE
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000591787300023
- Scopus ID
- 2-s2.0-84914684517
- Other Identifier
- 991019295189804721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic
- Nanoscience & Nanotechnology