You can also download this list as a TeX bib file.
- 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].
- 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].