Publications

This lists my publications and research of my PhD and other research to date.

PhD Thesis

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

Garland, J. and Gregg D. (2021) ‘HOBFLOPS CNNs: Hardware Optimized Bitslice-Parallel Floating-Point Operations for Convolutional Neural Networks’, Presented at the 21st Workshop on Compilers for Parallel Computing (CPC’2021) – Virtual Event – Presentation Slides
Garland, J. and Gregg D. (2019) ‘Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing’, Presented at HiPEAC 2019, the European Network of Excellence on High Performance and Embedded Architecture and Compilation Conference, 21-23 January 2019, Valencia, Spain – Presentation Slides
Garland, J. and Gregg D. (2017) ‘Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks’, Presented to the Science Foundation Ireland panel during their funding review – Presentation Slides

Anderson, A; Garland, J; Wen, Y; Barabasz, B; Persand, K; Vasudevan, A; Gregg, D. (2019) Chapter 6, ‘Hardware and software performance in deep learning’ in ‘Many-Core Computing: Hardware and Software’, pp: 141-161, ISBN: 978-1-78561-582-5
Garland, J. and Gregg D. (2017) ‘Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks’, in ACACES 2017 Poster Abstracts, 2017, pp. 53-56, ISBN: 978-88-905806-5-9.

The tech journalist Timothy Prickett Morgan of The Next Platform web site and Youtube channel interviewed me on Monday 27th July 2020 about our HOBFLOPS CNN work.

Garland, J. and Gregg D. (2018) ‘Low Complexity Multiply Accumulate Units for Convolutional Neural Networks with Weight-Sharing’, presented at HiPEAC, the European Network of Excellence on High Performance and Embedded Architecture and Compilation Conference, 21-23 January 2019, Valencia, Spain.
- @PROCEEDINGS{HiPEAC2019POSTER, author = {{Garland}, J. and {Gregg}, D.}, title = “{Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing}”, publisher = “HiPEAC, the European Network of Excellence on High Performance and Embedded Architecture and Compilation”, year = 2019, month = January, day = 23}
Garland, J. and Gregg D. (2017) ‘Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks’, presented at the Thirteenth International Summer School on Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES 2017), 9-15 July 2017, Fiuggi, Italy.
- @ARTICLE{ACACES2017POSTER, author = {{Garland}, J. and {Gregg}, D.}, title = “{Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks}”, publisher = “HiPEAC, the European Network of Excellence on High Performance and Embedded Architecture and Compilation ACACES 2017”, year = 2017, month = July, day = 12}

James Garland, ‘Structural Implementation of the CAN Communications Protocol Onto An ASIC Using VHDL ’, MSc Thesis, Department of Engineering, University of Central England, UK, December 1995.

Dr. Andrew Anderson, Mr. James Garland, Dr. YuanWen, Ms. Barbara Barabasz, Ms. Kaveena Persand, Dr. Aravind Vasudevan, and Dr. David Gregg. (2019) ‘Many-Core Computing: Hardware and Software’, ISBN: 978-1-78561-582-5. Chapter 6: ‘Hardware and software performance in deep learning’
Stephen Farrell and Vinny Cahill. (2006) ‘Delay- and Disruption-Tolerant Networking’, ISBN: 1596930632.
- Acknowledged by Stephen and Vinny in their book “Delay- and Disruption-Tolerant Networking” for the hardware research I assisted with when employed as a Research Assistant on the SeNDT Project at Trinity College Dublin, Ireland.