For the most up-to-date information on my publications, check out my Google Scholar and Twitter.
You can also download this list as a TeX bib file.
- J. Haris, P. Gibson, J. Cano, N. Bohm Agostini, and D. Kaeli, ‘SECDA-TFLite: A toolkit for efficient development of FPGA-based DNN accelerators for edge inference’, Journal of Parallel and Distributed Computing (JPDC), vol. 173, pp. 140–151, Mar. 2023, doi: 10.1016/j.jpdc.2022.11.005. [Paper] [Code]
N. Louloudakis, P. Gibson, J. Cano, A. Rajan, ‘Assessing Robustness of Image Recognition Models to Changes in the Computational Environment’, in NeurIPS ML Safety Workshop (MLSW) co-located with NeurIPS, Hybrid Conference, November-December 2022. [Paper]
P. Gibson, J. Cano, ‘Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation’, in 31st International Conference on Parallel Architectures and Compilation Techniques (PACT), Chicago, USA, October 2022. [Paper] [arXiv] [Code artifact]
A. Stjerngren, P. Gibson, and J. Cano, ‘Bifrost: End-to-End Evaluation and optimization of Reconfigurable DNN Accelerators’, in 2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), May 2022, pp. 288–299. doi: 10.1109/ISPASS55109.2022.00042. [Paper] [arXiv] [Code]
P. Gibson, J. Cano, ‘Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection’, in 4th Workshop on Accelerated Machine Learning (AccML) co-located with HiPEAC, Budapest, Hungary, June 2022. [Paper]
- J. Haris, P. Gibson, J. Cano, N. B. Agostini, and D. Kaeli, ‘SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference’, in 2021 IEEE 33rd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Oct. 2021, pp. 33–43. doi: 10.1109/SBAC-PAD53543.2021.00015. [Paper] [arXiv] [Code]
P. Gibson, J. Cano, J. Turner, E. J. Crowley, M. O’Boyle, and A. Storkey, ‘Optimizing grouped convolutions on edge devices’, in 2020 IEEE 31st international conference on application-specific systems, architectures and processors (ASAP), 2020, pp. 189–196. doi: 10.1109/ASAP49362.2020.00039. [Paper] [arXiv] [Code]
P. Gibson and J. Cano, ‘Orpheus: A new deep learning framework for easy deployment and evaluation of edge inference’, in 2020 IEEE international symposium on performance analysis of systems and software (ISPASS), 2020, pp. 229–230. doi: 10.1109/ISPASS48437.2020.00042. [Paper] [arXiv]