Publications

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.

N. Louloudakis, P. Gibson, J. Cano, and A. Rajan, ‘FetaFix: Automatic Fault Localization and Repair of Deep Learning Model Conversions’, in 29th International Conference on Evaluation and Assessment in Software Engineering (EASE), Istanbul, Turkey, June 2025. doi: 10.1145/3756681.3756931. [Paper] [arXiv] [Code]
N. Louloudakis, P. Gibson, J. Cano, and A. Rajan, ‘Exploring Robustness of Image Recognition Models on Hardware Accelerators’, in 18th IEEE International Conference on Software Testing, Verification and Validation (ICST), Mutation Workshop, April 2025. [arXiv]
A. Ferguson, P. Gibson, L. D’Agata, P. McLeod, F. Yaman, A. Das, I. Colbert, and J. Cano, ‘Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs’, in 5th Workshop on Machine Learning and Systems (EuroMLSys) co-located with EuroSys, April 2025. doi: 10.1145/3721146.3721948. [Paper] [arXiv]
P. Gibson, J. Cano, E. J. Crowley, A. Storkey, and M. O’Boyle, ‘DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration’, ACM Transactions on Architecture and Code Optimization (TACO), vol. 22, no. 1, pp. 1–28, March 2025. doi: 10.1145/3688609. [Paper] [arXiv]

N. Agostini, J. Haris, P. Gibson, Malith Jayaweera, Norm Rubin, Antonino Tumeo, José L. Abellán, José Cano, and David Kaeli ‘AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators’, in IEEE International Symposium on Code Generation and Optimization (CGO). [arXiv]

N. Louloudakis, P. Gibson, J. Cano, and A. Rajan ‘DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models’, in IEEE International Conference on Software Maintenance and Evolution (ICSME) [Paper] [arXiv].
W. Hu, P. Gibson, and J. Cano, ‘ICE-Pick: Iterative Cost-Efficient Pruning for DNNs’, in 40th International Conference on Machine Learning (ICML) (Neural Compression Workshop (NCW)). [Paper]
N. Louloudakis, P. Gibson, J. Cano, and A. Rajan ‘Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition’, to appear in IEEE/ACM International Conference on Automated Software Engineering (ASE).
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, and A. Rajan, ‘Assessing Robustness of Image Recognition Models to Changes in the Computational Environment’, in Conference on Neural Information Processing Systems (NeurIPS), ML Safety Workshop (MLSW), 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]