We accelerate PFNN (Phase-Functioned Neural Network) with TVM. The PFNN algorithm dynamically interpolates weights according to status of the game characters, which is not well supported by most neural network inference libraries. Therefore we reinterpret the compute equations and leverage TVM to implement a fast inference method for PFNN. Our methods show overwhelming performance advantage over traditional BLAS libraries.
This session is split into two parts, a 20 minute talk and a 10 minute community breakout session.