Chorus: Robust Multitasking Local Client-Server Collaborative Inference With Wi-Fi 6 for AIoT Against Stochastic Congestion Delay

The rapid growth of AIoT devices brings huge demands for DNNs deployed on resource-constrained devices. However, the intensive computation and high memory footprint of DNN inference make it difficult for the AIoT devices to execute the inference tasks efficiently. In many widely deployed AIoT use cases, multiple local AIoT devices launch DNN inference tasks randomly. Although local collaborative inference has been proposed to accelerate DNN inference on local devices with limited resources, multitasking local collaborative inference, which is common in AIoT scenarios, has not been fully studied in previous works. We consider multitasking local client-server collaborative inference (MLCCI), which achieves efficient DNN inference by offloading the inference tasks from multiple AIoT devices to a more powerful local server with parallel pipelined execution streams through Wi-Fi 6. Our optimization goal is to minimize the mean end-to-end latency of MLCCI. Based on the experiment results, we identify three key challenges: high communication costs, high model initialization latency, and congestion delay brought by task interference. We analyze congestion delay in MLCCI and its stochastic fluctuations with queuing theory and propose Chorus, a high-performance adaptive MLCCI framework for AIoT devices, to minimize the mean end-to-end latency of MLCCI against stochastic congestion delay. Chorus generates communication-efficient model partitions with heuristic search, uses a prefetch-enabled two-level LRU cache to accelerate model initialization on the server, reduces congestion delay and its short-term fluctuations with execution stream allocation based on the cross-entropy method, and finally achieves efficient computation offloading with reinforcement learning. We established a system prototype, which statistically simulated many virtual clients with limited physical client devices to conduct performance evaluations, for Chorus with real devices. The evaluation results for various workload levels show that Chorus achieved an average of $1.4\times$, $1.3\times$, and $2\times$ speedup over client-only inference, and server-only inference with LRU and MLSH, respectively.