Deep Learning Inference with Dynamic Graphs on Heterogeneous Platforms

One major drawback of deep-learning algorithms is the elevated cost of computing complexity and memory bandwidth required for inference. In order to ameliorate these costs in applications that utilize Convolutional Neural Networks (CNNs), a new, radical, approach is the dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. This conditional execution approach formulates a systematic and data-driven method for developing CNNs that are trained to eventually change size and form in real-time during inference, targeting to the smaller possible computational footprint. The conditional execution however, induces a number of challenges when it comes to the implementation of these algorithms to embedded systems. In this paper we present a systematic way of deploying this new dynamic pruning methodology, in heterogeneous platforms that facilitate both CPU and GPU subsystems. Realtime measurements of embedded implementations in modern SoCs verify the efficacy of the proposed methodology and demonstrate the ability of the dynamic networks to both adapt their size to the complexity of the task and deliver significant computational gains during inference.