The rapid rate of architectural change has placed enormous stress on compiler writers to keep pace with microprocessor evolution. This problem is compounded by the current trend to have multi-cores and multi-threading which makes such systems increasingly difficult to target. Also, current methods of designing computer systems will no longer be feasible in 10-15 years time; what is needed are new innovative approaches to architecture design that scale both with advances in underlying technology and with future application domains.
In recent years, several papers have been published showing great potential in constructing compilers and architectures using approaches such as machine learning and search.
The purpose of this workshop is to promote new ideas and to present recent developments in compiler and architecture design using machine learning, statistical approaches, and search in order to enhance their performance, scalability, and adaptability.
Topics of Interest (but not limited to):
Machine Learning, Statistical Approaches, or Search applied to
Feedback-Directed Compilation
Iterative Compilation
Dynamic Compilation/Adaptive Execution
Parallel Compiler Optimizations
Low-power Optimizations
Simulation
Performance Models
Processor and System Architecture
Design Space Exploration
Other Topics relevant to Intelligent andAdaptive Compilers/Architectures
Important dates:
Deadline for submission:
November 3, 2006
Decision notification:
December 19, 2006
Workshop:
January 28, 2007
Paper submission:
We invite two kinds papers:
Research papers with new results (15 page limit)
Short position/experience papers (5 page limit)
Important ammendment to publication procedure:
In order to increase the importance of this workshop, we will host all accepted papers on the workshop website.
Papers must be submitted in the PDF (preferably) or Postscript formats. Papers should normally be submitted using the link below, but can also be emailed to jcavazos@inf.ed.ac.uk . We suggest to use LNCS LaTeX templates that can be found at http://www.springeronline.com/lncs (go to "For Authors" and then "Information for LNCS Editors/Authors")
