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Towards Deep Learning on Resource-Constrained Robots: A Crowdsourcing Approach with Model Partition

Deep learning has shown great potential in promoting robot intelligence. However, a major obstacle to put it into practice is the huge computational complexity and memory consumption of neural networks. The onboard computers inside robots are usually of limited capability and cannot support the inference of large-scale neural networks with reasonable latency, a property essential to many tasks in the physical world. Moreover, because of the unstable or unacceptable network latency, it is hard to offload computation to powerful backend infrastructure (e.g., data centers) in many cases as well. To address this challenge, this paper proposes a novel "crowdsourcing" approach to enable efficient inference of large-scale deep learning models on a group of resource-constrained robots. "Crowdsourcing" means that the deep learning tasks on a single robot can exploit the idle computing power of robots in the proximity. This goal is achieved by splitting a network into several parts and distributing them to different robots with the help of the proposed latency prediction and optimal partition selection algorithms. The experiments show that our approach achieves on average 6.01X and up to 46.59X latency speedup in contrast with the approaches in which the models are executed locally on the robot or remotely on the cloud.