SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems

Abstract

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 ModelNet40, and show that the SplitNets framework achieves state-of-the-art (SOTA) performance and system latency compared with existing approaches.

Meng Li

Assistant Professor

I am currently a tenure-track assistant professor jointly affiliated with the Institute for Artificial Intelligence and School of Integrated Circuits in Peking University. My research interests focus on efficient and secure multi-modality AI acceleration algorithms and hardwares.