2025

  • V. Sharma, D. Pau, J. Cano ‘Biases in Edge Language Models: Detection, Analysis, and Mitigation’, to appear in EDGE AI Research Symposium 2025 (EDGEAI), Austin, Texas, February 2025.

2024

  • W. Hu, P. Henderson, J. Cano ‘DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations.’, Machine Learning with new Compute Paradigms Workshop (MLNCP) co-located with NeurIPS, Vancouver, Canada, December 2024. [Paper]

  • R. Saha, J. Haris, J. Cano ‘Accelerating PoT Quantization on Edge Devices’, to appear in 31st IEEE International Conference on Electronics Circuits and Systems (ICECS), Nancy, France, November 2024. [Paper]

  • V. Sharma, D. Pau and J. Cano ‘Efficient Tiny Machine Learning for Human Activity Recognition on Low-Power Edge Devices’, to appear in IEEE Research and Technologies for Society and Industry (RTSI), Lecco, Italy, September 2024. [Paper]

  • P. Gibson, J. Cano, E. J. Crowley, A. Storkey, M. O’Boyle ‘DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration’, ACM Transaction on Architecture and Code Optimization (TACO), September 2024. [Paper]

  • J. Haris, R. Saha, W. Hu, J. Cano ‘Designing Efficient LLM Accelerators for Edge Devices’, in Workshop on New Approaches for Addressing the Computing Requirements of LLMs and GNSs (ARC-LG), co-located with ISCA, Buenos Aires, Argentina, June 2024. [Paper]

  • N. Bohm Agostini, J. Haris, P. Gibson, M. Jawaweera, N. Rubin, Antonio Tumeo, J. L. Abellán, J. Cano, D. Kaeli ‘AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators’, in IEEE/ACM International Symposium on Code Generation and Optimization (CGO), Edinburgh, UK, March 2024. [Paper] [arXiv]

  • J. Haris, N. Bohm Agostini, A. Tumeo, D. Kaeli, J. Cano ‘Data Transfer Optimizations for Host-CPU and Accelerators in AXI4MLIR’, in 5th Compilers for Machine Learning Workshop (C4ML), co-located with CGO, Edinburgh, UK, March 2024. [Paper]

2023

  • P. Gibson ‘Compiler-centric Across-stack Deep Learning Acceleration’, Ph.D. Thesis, University of Glasgow. [Thesis]

  • N. Louloudakis, P. Gibson, J. Cano, and A. Rajan ‘DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models’, to appear in IEEE International Conference on Software Maintenance and Evolution (ICSME), Track, Bogtá, Colombia, October 2023. [Paper] [arXiv]

  • 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), September 2023. [Paper] [arXiv]

  • W. Hu, P. Gibson, and J. Cano, ‘ICE-Pick: Iterative Cost-Efficient Pruning for DNNs’, in Neural Compression Workshop (NCW), co-located with ICML, Honolulu, Hawaii, USA, July 2023. [Paper]

  • F. Ayaz, I. Zakariyya, J. Cano, S. L. Keoh, J. Singer, D. Pau, M. Kharbouche-Harrari, ‘Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks’, in International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, June 2023. [Pre-print]

  • J. Haris, P. Gibson, J. Cano, N. Bohm Agostini, D. Kaeli, ‘SECDA-TFLite: A Toolkit for Efficient Development of FPGA-based DNN Accelerators for Edge Inference’, in Elsevier Journal of Parallel and Distributed Computing (JPDC), Volume 173, March 2023. [Paper] [Code]

2022

  • 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. [Pre-print]

  • 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]

  • 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]

  • A. Stjerngren, P. Gibson, J. Cano, ‘Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators’, in IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), Singapore, May 2022. [Paper] [arXiv] [Code]

2021

  • S. Dong, Y. Sun, N. Bohm Agostini, E. Karimi, D. Lowell, J. Zhou, J. Cano, J. L. Abellán, D. Kaeli, ‘Spartan: A Sparsity-Adaptive Framework to Accelerate Deep Neural Network Training on GPUs’, in IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 32, Issue 10, October 2021. [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), Belo Horizonte, Brazil, October 2021. [Paper] [arXiv] [Code]

  • M. Lofqvist, J. Cano, ‘Optimizing Data Processing in Space for Object Detection in Satellite Imagery’, in 35th Annual Small Satellite Conference (SmallSat), Virtual Event, August 2021. [Paper] [arXiv]

2020

  • N. Bohm Agostini, S. Dong, E. Karimi, M. Torrents, J. Cano, J. L. Abellán, D. Kaeli, ‘Design Space Exploration of Accelerators and End-to-End DNN Evaluation with TFLITE-SOC’, in 32nd IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Porto, Portugal, September 2020. [Paper]

  • 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), Virtual Meeting, August 2020. [Paper] [arXiv]

  • M. Lofqvist, J. Cano, ‘Accelerating Deep Learning Applications in Space’, in 34th Annual Small Satellite Conference (SmallSat), Virtual Event, August 2020. [Paper] [arXiv]

  • 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), Manchester, UK, July 2020. [Paper] [arXiv] [Code]