Modern deep neural networks are powerful and widely applicable models that
extract task-relevant information through multi-level abstraction. Their
cross-domain success, however, is often achieved at the expense of
computational cost, high memory bandwidth, and long inference latency, which
prevents their deployment in resource-constrained and time-sensitive scenarios,
such as edge-side inference and self-driving cars. While recently developed
methods for creating efficient deep neural networks are making their real-world
deployment more feasible by reducing model size, they do not fully exploit
input properties on a per-instance basis to maximize computational efficiency
and task accuracy. In particular, most existing methods typically use a
one-size-fits-all approach that identically processes all inputs. Motivated by
the fact that different images require different feature embeddings to be
accurately classified, we propose a fully dynamic paradigm that imparts deep
convolutional neural networks with hierarchical inference dynamics at the level
of layers and individual convolutional filters/channels. Two compact networks,
called Layer-Net (L-Net) and Channel-Net (C-Net), predict on a per-instance
basis which layers or filters/channels are redundant and therefore should be
skipped. L-Net and C-Net also learn how to scale retained computation outputs
to maximize task accuracy. By integrating L-Net and C-Net into a joint design
framework, called LC-Net, we consistently outperform state-of-the-art dynamic
frameworks with respect to both efficiency and classification accuracy. On the
CIFAR-10 dataset, LC-Net results in up to 11.9$\times$ fewer floating-point
operations (FLOPs) and up to 3.3% higher accuracy compared to other dynamic
inference methods. On the ImageNet dataset, LC-Net achieves up to 1.4$\times$
fewer FLOPs and up to 4.6% higher Top-1 accuracy than the other methods.

提出了一种全动态模型的方法，称为 LC-Net，旨在最大化深度卷积神经网络的计算效率和任务准确性，通过以层和卷积滤波通道为单位的层次推理动力学，能够预测冗余层和卷积滤波器 / 通道并学习保留计算结果以实现最大化任务准确性。