Orchestrating Multimodal DNN Workloads in Wireless Neural Processing
In edge inference, wireless resource allocation and accelerator-level deep neural network (DNN) scheduling have yet to be co-optimized in an end-to-end manner. The lack of coordination between wireless transmission and accelerator-level DNN execution prevents efficient overlap, leading to higher end-to-end inference latency. To address this issue, this paper investigates multimodal DNN workload orchestration in wireless neural processing (WNP), a paradigm that integrates wireless transmission and multi-core accelerator execution into a unified end-to-end pipeline. First, we develop a unified communication-computation model for multimodal DNN execution and formulate the corresponding optim...
| Authors | Sai Xu, Kai-Kit Wong, Yanan Du et al. |
|---|---|
| Primary category | eess.SP |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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