We design deep neural networks (DNNs) and corresponding networks' splittings
to distribute DNNs' workload to camera sensors and a centralized aggregator on
head mounted devices to meet system performance targets in inference accuracy
and latency under the given hardware resource constraints. To achieve an
optimal balance among computation, communication, and performance, a
split-aware neural architecture search framework, SplitNets, is introduced to
conduct model designing, splitting, and communication reduction simultaneously.
We further extend the framework to multi-view systems for learning to fuse
inputs from multiple camera sensors with optimal performance and systemic
efficiency. We validate SplitNets for single-view system on ImageNet as well as
multi-view system on 3D classification, and show that the SplitNets framework
achieves state-of-the-art (SOTA) performance and system latency compared with
existing approaches.

在给定的硬件资源约束下，我们设计了深度神经网络，对应的网络拆分来将 DNN 的工作负载分配到摄像头传感器和中央聚合器上的头戴设备上，以满足系统性能目标，通过 SplitNets 框架进行模型设计、拆分和通信减少，进一步扩展框架用于多视图系统，以实现从多个相机传感器中输入的最佳性能和系统效率的融合学习，并在 ImageNet 和 3D 分类的单视图及多视图系统上验证拆分网络的表现优于现有方法。