  Achieving faster execution with shorter compilation time can foster further
diversity and innovation in neural networks. However, the current paradigm of
executing neural networks either relies on hand-optimized libraries,
traditional compilation heuristics, or very recently genetic algorithms and
other stochastic methods. These methods suffer from frequent costly hardware
measurements rendering them not only too time consuming but also suboptimal. As
such, we devise a solution that can learn to quickly adapt to a previously
unseen design space for code optimization, both accelerating the search and
improving the output performance. This solution dubbed Chameleon leverages
reinforcement learning whose solution takes fewer steps to converge, and
develops an adaptive sampling algorithm that not only focuses on the costly
samples (real hardware measurements) on representative points but also uses a
domain-knowledge inspired logic to improve the samples itself. Experimentation
with real hardware shows that Chameleon provides 4.45x speed up in optimization
time over AutoTVM, while also improving inference time of the modern deep
networks by 5.6%.
