  By adding exiting layers to the deep learning networks, early exit can
terminate the inference earlier with accurate results. The passive
decision-making of whether to exit or continue the next layer has to go through
every pre-placed exiting layer until it exits. In addition, it is also hard to
adjust the configurations of the computing platforms alongside the inference
proceeds. By incorporating a low-cost prediction engine, we propose a
Predictive Exit framework for computation- and energy-efficient deep learning
applications. Predictive Exit can forecast where the network will exit (i.e.,
establish the number of remaining layers to finish the inference), which
effectively reduces the network computation cost by exiting on time without
running every pre-placed exiting layer. Moreover, according to the number of
remaining layers, proper computing configurations (i.e., frequency and voltage)
are selected to execute the network to further save energy. Extensive
experimental results demonstrate that Predictive Exit achieves up to 96.2%
computation reduction and 72.9% energy-saving compared with classic deep
learning networks; and 12.8% computation reduction and 37.6% energy-saving
compared with the early exit under state-of-the-art exiting strategies, given
the same inference accuracy and latency.
