  Deploying deep learning (DL) on mobile devices has been a notable trend in
recent years. To support fast inference of on-device DL, DL libraries play a
critical role as algorithms and hardware do. Unfortunately, no prior work ever
dives deep into the ecosystem of modern DL libs and provides quantitative
results on their performance. In this paper, we first build a comprehensive
benchmark that includes 6 representative DL libs and 15 diversified DL models.
We then perform extensive experiments on 10 mobile devices, which help reveal a
complete landscape of the current mobile DL libs ecosystem. For example, we
find that the best-performing DL lib is severely fragmented across different
models and hardware, and the gap between those DL libs can be rather huge. In
fact, the impacts of DL libs can overwhelm the optimizations from algorithms or
hardware, e.g., model quantization and GPU/DSP-based heterogeneous computing.
Finally, atop the observations, we summarize practical implications to
different roles in the DL lib ecosystem.
