Title: Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution,
Abstract: We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward
deep neural network that allows selective execution. Given an input, only a
subset of D2NN neurons are executed, and the particular subset is determined by
the D2NN itself. By pruning unnecessary computation depending on input, D2NNs
provide a way to improve computational efficiency. To achieve dynamic selective
execution, a D2NN augments a feed-forward deep neural network (directed acyclic
graph of differentiable modules) with controller modules. Each controller
module is a sub-network whose output is a decision that controls whether other
modules can execute. A D2NN is trained end to end. Both regular and controller
modules in a D2NN are learnable and are jointly trained to optimize both
accuracy and efficiency. Such training is achieved by integrating
backpropagation with reinforcement learning. With extensive experiments of
various D2NN architectures on image classification tasks, we demonstrate that
D2NNs are general and flexible, and can effectively optimize
accuracy-efficiency trade-offs.