Delighted to say that I passed my PhD Viva yesterday after a 3.5-hour viva examination. With thanks to my examiners Dr Georgios Karakonstantis and Dr Michael Manzke and chaired by Prof Khurshid Ahmad. And a special thanks to my adviser Prof. David Gregg. It’s been a blast!
And a big, big thanks for the love and support of lab mates, friends and family. And most of all I couldn’t have done it without the support of my wife Michelle, and kids Toby and Zara.
I’m delighted to announce that the book “Many-Core Computing: Hardware and Software” has been published today by the Institution of Engineering and Technology (IET). I, along with Dr. Andrew Anderson, Dr. YuanWen, Barbara Barabasz, Kaveena Persand, Dr. Aravind Vasudevan, and Dr. David Gregg have written chapter 6 entitled “Hardware and software performance in deep learning”.
Go on, treat yourselves, and rush out and get a copy from the IET web site (local link on my Publications page) and tell all your friends and family too!
If I still have your attention, here’s the publisher’s official description: “Computing has moved away from a focus on performance-centric serial computation, instead towards energy-efficient parallel computation. This provides continued performance increases without increasing clock frequencies, and overcomes the thermal and power limitations of the dark-silicon era. As the number of parallel cores increases, we transition into the many-core computing era. There is considerable interest in developing methods, tools, architectures and applications to support many-core computing. The primary aim of this edited book is to provide a timely and coherent account of the recent advances in many-core computing research. Starting with programming models, operating systems and their applications; the authors present runtime management techniques, followed by system modelling, verification and testing methods, and architectures and systems. The book ends with some examples of innovative applications. “
And that’s the PASM paper presented.
Lots of interest and very good questions from the audience and the session chair Luca Fanucci (Università di Pisa @Unipisa). Thoroughly enjoyed it. #HiPEAC19#hipeac2019
I am presenting my second Ph.D. publication entitled “Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing” at HiPEAC 2019.
I will be presenting in Session 12 Programming Models, Neural Networks.
If you can’t make the talk, I’ll also be presenting a poster of the paper in the Student Poster Session.
Finally, if you can’t make the conference, you can still read my paper for free on the ACM TACO web site.
Dr. David Gregg and I have had my second Ph.D. paper published. Entitled “Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing”, the ACM Transactions on Architecture and Code Optimization published it in their August 2018 Journal. The ACM also published it on their online server, which can be viewed for free! It can also be found on the arXiv.org pre-print server.
Here are more details of the publication:
Title: Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing
Published in: ACM Transactions on Architecture and Code Optimization (TACO) (Volume: 15, Issue: 3, Article 31)
Date of Publication: August 2018
Print ISSN: 1544-3566
DOI: 10.1145/3233300
Publisher: ACM
ACM TACO Bibtex (should you wish to cite our paper):
@article{Garland:2018:LCM:3274266.3233300,
author = {Garland, James and Gregg, David},
title = {Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing},
journal = {ACM Trans. Archit. Code Optim.},
issue_date = {August 2018},
volume = {15},
number = {3},
month = sep,
year = {2018},
issn = {1544-3566},
pages = {31:1--31:24},
articleno = {31},
numpages = {24},
url = {http://doi.acm.org/10.1145/3233300},
doi = {10.1145/3233300},
acmid = {3233300},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {ASIC, CNN, FPGA, arithmetic hardware circuits, multiply accumulate, power efficiency},
}
Published in: arXiv.org. arXiv Bibtex:
@article{DBLP:journals/corr/abs-1801-10219,
author = {James Garland and David Gregg},
title = {Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing},
journal = {CoRR},
volume = {abs/1801.10219},
year = {2018},
url = {http://arxiv.org/abs/1801.10219},
archivePrefix = {arXiv},
eprint = {1801.10219},
timestamp = {Mon, 13 Aug 2018 16:47:23 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1801-10219},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
My complete list of publications can also be seen on my research publications page.
I undertook my Ph.D. Confirmation viva voce today (6th Feb 2018). This entailed a presentation and a report to two professors, Dr. Jonathan Dukes (presentation chair) and Dr. Michael Manzke (domain expert) of Trinity College Dublin who questioned me during and after the presentation. After a short discussion with my supervisor Dr. David Gregg, they confirmed me to the Ph.D. register and therefore become a Ph.D. candidate.
Dr. David Gregg and I have had my first paper of my PhD, entitled “Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks” published. The IEEE Computer Architecture Letters published it on 23 January 2017. Whilst waiting for the printed version, the IEEE has published it on their on-line pre-print server. It can also be found on the arXiv.org pre-print server.
Here are more details of the publication:
Published in: IEEE Computer Architecture Letters ( Volume: PP, Issue: 99)
Date of Publication: 23 January 2017
Print ISSN: 1556-6056
DOI: 10.1109/LCA.2017.2656880
Publisher: IEEE
Sponsored by: IEEE Computer Society
Published in: arXiv.org
@ARTICLE{2016arXiv160905132G,
author = {{Garland}, J. and {Gregg}, D.},
title = "{Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1609.05132},
keywords = {Computer Science - Neural and Evolutionary Computing},
year = 2016,
month = aug,
adsurl = {http://adsabs.harvard.edu/abs/2016arXiv160905132G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
My complete list of publications can also be seen on my research publications page.
Dr. David Gregg and I have had my first PhD paper accepted by the the pre-print server arXiv (pronounced archive). The paper, entitled “Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks” is a 4 page paper, the PDF for which can be found by searching arxiv.org and directly at Comments welcome!
