  Dataflow/mapping decides the compute and energy efficiency of DNN
accelerators. Many mappers have been proposed to tackle the intra-layer
map-space. However, mappers for inter-layer map-space (aka layer-fusion
map-space), have been rarely discussed. In this work, we propose a mapper,
DNNFuser, specifically focusing on this layer-fusion map-space. While existing
SOTA DNN mapping explorations rely on search-based mappers, this is the first
work, to the best of our knowledge, to propose a one-shot inference-based
mapper. We leverage Transformer as our DNN architecture to learn layer-fusion
optimization as a sequence modeling problem. Further, the trained DNNFuser can
generalize its knowledge and infer new solutions for unseen conditions. Within
one inference pass, DNNFuser can infer solutions with compatible performance to
the ones found by a highly optimized search-based mapper while being 66x-127x
faster.
