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. “
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 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!