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.