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I develop a novel methodology and open-source technology to collaboratively optimize deep learning powered by CK across the whole SW/HW stack including DNN engines, libraries, models and data sets across diverse devices from IoT to supercomputers!

Designing efficient many-core computer systems in terms of performance, energy, size, reliability, cost, etc. becomes intolerably complex, ad-hoc, costly and error prone due to limitations of available technology, enormous number of available design and optimization choices, and complex interactions between all software and hardware components.

Worse, unlike other mature sciences, computer engineering lacks common research and experimentation methodology as well as unified mechanisms for knowledge building and exchange apart from publications where reproducibility of results is often not even considered.

After completely switching my research to computer engineering in 1998, I have been working on preparing foundations for a collaborative, systematic and reproducible research and experimental methodology and publication model where experimental results and all related material (code,data and experimental workflows) is continuously shared, discussed, validated and improved by the community.

Since it was extremely difficult to persuade the community in the importance of such approach, I started setting up an example sharing all my past research artifacts including hundreds of benchmarks, kernels, numerical applications, data sets, predictive models, universal experimental analysis and auto-tuning pipelines, self-tuning machine learning based meta compiler, and unified statistical analysis and machine learning plugins along with publications using my public frameworks (cTuning V1, cTuning V3 aka cM, cTuning V4 aka CK) and live CK-powered repository of knowledge.

You can find further info about my long-term vision and foundation of collaborative and reproducible analysis, optimization and co-design of computer systems in the following publications [CPC'15, Scientific Programming'14, TRUST@PLDI'14, GCC Summit'09] and on this page.

Since 2006, my methodology and infrastructure has been used in multiple academic and industrial projects and lectures:

I continue improving my technology and methodology for collaborative and reproducible experimentation. Hence, if your organization is interested in systematizing ad-hoc research and experimentation and connecting it to "big data" predictive analytics, or interested in guest lectures, talks and tutorials about my research and open-source cTuning technology, or interested to establish common projects and possibly interdisciplinary labs, do not hesitate to get in touch. At the moment, I often commute to UK/USA, and hence primerily interested in opportunities there, but open to other interesting possibilities too.


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