  With the rapid development of deep learning, recent research on intelligent
and interactive mobile applications (e.g., health monitoring, speech
recognition) has attracted extensive attention. And these applications
necessitate the mobile edge computing scheme, i.e., offloading partial
computation from mobile devices to edge devices for inference acceleration and
transmission load reduction. The current practices have relied on collaborative
DNN partition and offloading to satisfy the predefined latency requirements,
which is intractable to adapt to the dynamic deployment context at runtime.
AdaMEC, a context-adaptive and dynamically-combinable DNN deployment framework
is proposed to meet these requirements for mobile edge computing, which
consists of three novel techniques. First, once-for-all DNN pre-partition
divides DNN at the primitive operator level and stores partitioned modules into
executable files, defined as pre-partitioned DNN atoms. Second,
context-adaptive DNN atom combination and offloading introduces a graph-based
decision algorithm to quickly search the suitable combination of atoms and
adaptively make the offloading plan under dynamic deployment contexts. Third,
runtime latency predictor provides timely latency feedback for DNN deployment
considering both DNN configurations and dynamic contexts. Extensive experiments
demonstrate that AdaMEC outperforms state-of-the-art baselines in terms of
latency reduction by up to 62.14% and average memory saving by 55.21%.
