Abstract: Reconfigurable dataflow architecture is an emerging design for deep learning training accelerator. This architecture maps model operators to an accelerator in a spatial way, enabling pipeline parallelization for high throughput. An essential ingredient to exploit this throughput advantage is compiler Performance Optimization (PO) which searches for optimal model mappings. The convention in industry-leading dataflow compilation uses hand-tuned rules to guide PO, requiring immense engineering cost to develop. This paper challenges this convention and asks if data-driven learned performance optimization can reduce the engineering cost while improving training throughput over hand-tuned rules. We present a workflow which guides PO using simple machine learning models trained from throughput observations of randomly generated mappings.