Peer-Reviewed Journal Articles
- Haider M Al Juboori, James Garland. Laser Beam Propagation Features via Atmospheric Turbulence-Induced Beam Wander: Testbed Conceptual Design. COAT-2023 – workshop (Communications and Observations through Atmospheric Turbulence), Mar 2023, Durham (GB), United Kingdom. ⟨hal-04246206⟩
- Ryan Furlong, Vincent O’Brien, James Garland et al. Feature Representation in Pretrained Deep Metric Embeddings, 05 April 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2761696/v1]
- L. Connolly, D. O’Gorman, J. Garland and E. F. Tobin, “Spatial Mapping of light aircraft with stereo-vision camera for use on Unmanned Aircraft System for defect localisation,” 2023 International Conference on Unmanned Aircraft Systems (ICUAS), Warsaw, Poland, 2023, pp. 413-418, doi: 10.1109/ICUAS57906.2023.10155987.
- James Garland, David Gregg. HOBFLOPS for CNNs: Hardware Optimized Bitslice-Parallel Floating-Point Operations for Convolutional Neural Networks, 02 September 2021, PREPRINT (Version 1) available at Research Square DOI: 10.21203/rs.3.rs-866039/v1
- Syed Asad Alam, James Garland, and David Gregg. 2021. Low-precision Logarithmic Number Systems: Beyond Base-2. ACM Transactions on Architecture and Code Optimisation (TACO) 18, 4, Article 47 (December 2021), 25 pages. DOI 10.1145/3461699
- Garland, J. and Gregg D. (2018) ‘Low Complexity Multiply Accumulate Units for Convolutional Neural Networks with Weight-Sharing’, in ACM Transactions on Architecture and Code Optimisation (TACO), vol. 15, no. 3, August 2018, Article 31, pp. 1-24, DOI: 10.1145/3233300 – Preprint
- Garland, J. and Gregg D. (2017) ‘Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks’, in IEEE Computer Architecture Letters, vol. 16, no. 2, pp. 132-135, July-Dec. 1 2017, DOI: 10.1109/LCA.2017.2656880 – Preprint