  The process of optimizing the latency of DNN operators with ML models and
hardware-in-the-loop, called auto-tuning, has established itself as a pervasive
method for the deployment of neural networks. From a search space of
loop-optimizations, the candidate providing the best performance has to be
selected. Performance of individual configurations is evaluated through
hardware measurements. The combinatorial explosion of possible configurations,
together with the cost of hardware evaluation makes exhaustive explorations of
the search space infeasible in practice. Machine Learning methods, like random
forests or reinforcement learning are used to aid in the selection of
candidates for hardware evaluation. For general purpose hardware like x86 and
GPGPU architectures impressive performance gains can be achieved, compared to
hand-optimized libraries like cuDNN. The method is also useful in the space of
hardware accelerators with less wide-spread adoption, where a high-performance
library is not always available. However, hardware accelerators are often less
flexible with respect to their programming which leads to operator
configurations not executable on the hardware target. This work evaluates how
these invalid configurations affect the auto-tuning process and its underlying
performance prediction model for the VTA hardware. From these results, a
validity-driven initialization method for AutoTVM is developed, only requiring
41.6% of the necessary hardware measurements to find the best solution, while
improving search robustness.
