Difference between revisions of "Project Plan"

End date: 31 August 2013
Work Package Leader: Embecosm
Contributing Partners: Embecosm

• Definition of compiler and hardware independent interface for machine learning compiler.
• Selection of a set of software characteristics to be exploited during the optimisation selection process.
• Identify target for first implementation, GCC or LLVM.

• Identify target for first implementation, GCC or LLVM.
• Determine degree of integration with specific compilers.
• Identify machine learning interface.
• Identify available features which can be used as inputs.
• Iterative refinement on 2-4.

• General computing infrastructure

• None

• Work Package 2

End date: 30 Nov 2014
Work Package Leader: Embecosm
Contributing Partners: Embecosm

• Implementation of prototype framework with one compiler, identified in Work Package 1
• Implementation of prototype framework with other compiler.

• Write code for use with first compiler.
• Write documentation for use with first compiler.
• Implement regression tests for use with first compiler.
• Extend support for use with second compiler (code, documentation, regression).
• Iterative refinement on 1-4.

• General computing infrastructure

• Work Package 1

• None

End date: 31 July 2013
Work Package Leader: University of Bristol
Contributing Partners: University of Bristol

• Implementation of Hardware Measurement

• Design board, reusing existing expertise
• Board Implementation
• Board testing

• PCB Manufacturing (outsourced)

• None

• Work Package 4 (to instrument boards)

End date: 30 September 2013
Work Package Leader: University of Bristol
Contributing Partners: University of Bristol, Embecosm

• Creation of a set of applications for training.
• Creation of a set of applications for initial testing of trained systems.
• Development of larger case studies for testing purposes.
• Selection of target embedded systems for testing.

• Select suitable test and training applications from existing benchmark suites.
• Choice of case studies from wider community.
• Selection of embedded systems representative of industrial/commercial applications in consultation with community.
• Integration of embedded systems with hardware test platform (Work Package 3)

• Selection of embedded systems
• Community engagement platforms

• None

• None

End date: 31 August 2014
Work Package Leader: University of Bristol
Contributing Partners: University of Bristol, Embecosm

• Understand current machine learning techniques and decide if relevant
• Select approach(es) for incorporating into framework
• Refinement in the light of ongoing project development and experience

• Review existing uses including MILEPOST, directed learning, abductive learning (Literature)
• Whole team working days to bring together theory with implementers to select approach and specify API
• Decision on choice of training approach, e.g. FFD, random, etc.
• Iterative review during second year of programme, inc. potential for reordering

• General computing infrastructure

• None

• None

End date: 31 August 2013
Work Package Leader: University of Bristol
Contributing Partners: University of Bristol, Embecosm

• Theoretical analysis of why energy optimisations work.

• Look at all existing work with energy measurement of relevant systems to look for data to guide implementers of optimisation passes to implement those specific for energy minimisation.

• General computing infrastructure

• None

• None

End date: 31 May 2014
Work Package Leader: Embecosm
Contributing Partners: Embecosm

• Set up of training infrastructure and demonstration with tests using existing optimisations
• Refine/repeat for use with new optimisations

• Set up infrastructure for existing optimisations
• Train infrastructure with small set with existing optimisations
• Test with small set with existing optimisations
• Repeat above with new optimisations from Work Package 8

• High performance workstation
• Embedded systems with hardware energy measuring

• Work Package 2 (Compiler Infrastructure)
• Work Package 4 (Test hardware, test applications)
• Work Package 8 (New Optimisations)

• None

End date: 31 May 2014
Work Package Leader: Embecosm
Contributing Partners: Embecosm

• Design and implement optimisation passes in the GCC and LLVM compilers.

• Design new optimisation passes in light of theory from Work Package 6
• Implement new optimisation passes in GCC
• Reimplement optimisation passes in LLVM

• General computing infrastructure

• None

• None

End date: 30 Nov 2014
Work Package Leader: University of Bristol
Contributing Partners: University of Bristol, Embecosm

• Full evaluation of infrastructure using full training sets, full test sets and large case studies for both with and without our new optimisations.

• Train infrastructure with existing optimisations
• Evaluate with smaller tests with existing optimisations
• Evaluate with case studies with existing optimisations
• Repeat above with new optimisations
• Write paper detailing findings
• Review and refine paper

• Instrumented embedded systems
• HPC Facilities

• None

• None

End date: 30 Nov 2014
Work Package Leader: University of Bristol, Embecosm
Contributing Partners: University of Bristol, Embecosm

• Business case development inc. market analysis
• Engagement with relevant communities
• Engagement with potential customers
• Academic and business publications

• Develop business case by engagement of all stakeholders
• Ongoing review of business case throughout project, leading to updated exploitation plan
• Engagement with the technical community through participation in workshops (including EACO, NMI, etc.), presentations, training events, new media using the skills of AB Open
• Engagement with potential customers
• Publication of papers as described in various Work Packages.

• General computing infrastructure, including website and social media (http://mageec.org)

• None

• None