Making computer engineering a science;
Systematizing program and system analysis and optimization using auto-tuning, machine learning and social networking.
The main aim of cTuning virtual lab is to promote collaborative and reproducible research in computer engineering to build efficient, adaptive, self-tuning computer systems.
Our original research on semiconductor neural networks for future adaptive, neural computers has been considerably slowed down in 1993-1996 due to slow and inefficient computer systems. It forced us to switch to computer engineering to understand how to build fast, reliable and power-efficient computer systems and to apply our background in physics and AI to systematize and automate design and optimization of computer systems.
We decided to open cTuning public center and virtual lab to collaboratively develop tools, repositories, models and share data and interfaces to gradually learn how to build efficient computers that should allow us to get back to original research on neural computers one day.
Since 1997, we have been involved in multiple collaborative projects while releasing our open-source tools and data from our publications to the community. We are advocating for a new publication model that should favor reproducible research and sharing of data, models, tools and interfaces.
We are grateful to all our collaborators for interesting discussions, support and feedback!