Research output per year
Research output per year
Project Details
Layman's description
For many computer-based systems it is important to understand the worst-case behaviour of systems, e.g. how long it takes for a car's braking system takes to activate. This project has developed techniques for this.
Key findings
The project established important machine learning and search-based techniques for Worst-Case Execution Time (WCET). The principal results were machine learning techniques for exactly determining the loop bounds of software and understanding cache behaviours, and how to achieve better confidence when searching for the WCET.
| Status | Finished |
|---|---|
| Effective start/end date | 1/10/07 → 30/09/11 |
Funding
- EPSRC: £379,941.00
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Accurate Determination of Loop Iterations for Worst-Case Execution Time Analysis
Bartlett, M., Bate, I. & Kazakov, D., 1 Nov 2010, In: IEEE Transactions on Computers. 59, 11, p. 1520-1532 13 p., 5432156.Research output: Contribution to journal › Article › peer-review
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Instruction Cache Prediction Using Bayesian Networks
Bartlett, M., Bate, I. & Cussens, J., 1 Aug 2010, Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010). Lisbon, Portugal: IOS Press, Vol. 215. p. 1099-1100 2 p. (Frontiers in Artificial Intelligence and Applications; vol. 215).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Learning Bayesian Networks for Improved Instruction Cache Analysis
Bartlett, M., Bate, I. & Cussens, J., 2010, 2010 Ninth International Conference on Machine Learning and Applications: Proceedings of the 9th International Conference on Machine Learning and Applications. IEEE, p. 417-423 7 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution