CANS: Accelerating Multiuser Collaborative Edge Inference via Cooperative Autodidactic NeuroSurgeon
arXiv:2606.09175v1 Announce Type: new Abstract: Recently, mobile edge computing (MEC)-enabled collaborative deep neural network (DNN) inference has emerged as a promising approach for delivering intelligent services to resource-constrained mobile devices. A representative scenario is multi-user collaborative edge inference, where distinct devices independently partition their DNN models and offload backend computation to a common edge server over wireless networks. However, determining the optimal DNN partition for each device is challenging due to unknown and time-varying system conditions, i
The proliferation of edge devices and increasing demand for sophisticated AI applications on resource-constrained hardware necessitate new methods for efficient distributed inference.
Optimizing collaborative edge inference is crucial for scaling AI services to mobile and IoT devices, impacting sectors from manufacturing to consumer electronics.
This research outlines a method to dynamically optimize DNN partitioning and offloading for multi-user edge inference, improving efficiency and adaptability in distributed AI systems.
- · Mobile edge computing providers
- · AI-powered device manufacturers
- · Telecommunications companies
- · Cloud computing providers
- · Traditional centralized cloud inference models
- · Devices with limited on-device AI capabilities
Improved performance and reduced latency for AI applications on mobile and edge devices.
Accelerated adoption of more complex AI models in resource-constrained environments, expanding the reach of AI services.
Enhanced utility and capability of IoT ecosystems, leading to new service models and data-driven industries.
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Read at arXiv cs.LG