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Compiler for Machine Learning for Embedded Programs Optimisation

The MILEPOST project aims to develop compiler technology that can automatically learn how to best optimise programs for re-configurable heterogeneous embedded processors. If successful we will be able to dramatically reduce the time to market of re-configurable systems. Rather than developing a specialised compiler by hand for each configuration, our project will produce optimising compilers automatically. Current handcrafted approaches to compiler development are no longer sustainable. With each generation of re-configurable architecture, the compiler development time increases and the performance improvement achieved decreases. As high performance embedded systems move from application specific ASICs to programmable heterogeneous processors, this problem is becoming critical.

This project explores an emerging alternative approach where we use machine-learning techniques, developed in the artificial intelligence arena, to learn how to generate compilers automatically. Such an approach, if successful, will have a dramatic impact on re-configurable systems. This means that for a fixed amount of design time. We can evaluate many more configurations leading to better and more cost-effective performance. If successful, this will enable Europe to increase its dominance in this critical emerging market.

MILEPOST GCC is the machine learning based compiler from the project.

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