  For practical deep neural network design on mobile devices, it is essential
to consider the constraints incurred by the computational resources and the
inference latency in various applications. Among deep network acceleration
related approaches, pruning is a widely adopted practice to balance the
computational resource consumption and the accuracy, where unimportant
connections can be removed either channel-wisely or randomly with a minimal
impact on model accuracy. The channel pruning instantly results in a
significant latency reduction, while the random weight pruning is more flexible
to balance the latency and accuracy. In this paper, we present a unified
framework with Joint Channel pruning and Weight pruning (JCW), and achieves a
better Pareto-frontier between the latency and accuracy than previous model
compression approaches. To fully optimize the trade-off between the latency and
accuracy, we develop a tailored multi-objective evolutionary algorithm in the
JCW framework, which enables one single search to obtain the optimal candidate
architectures for various deployment requirements. Extensive experiments
demonstrate that the JCW achieves a better trade-off between the latency and
accuracy against various state-of-the-art pruning methods on the ImageNet
classification dataset. Our codes are available at
https://github.com/jcw-anonymous/JCW.
