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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.11150 |
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| _version_ | 1866912644448911360 |
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| author | Xu, Sai Du, Yanan |
| author_facet | Xu, Sai Du, Yanan |
| contents | This article presents a wireless neural processing architecture (WiNPA), providing a novel perspective for accelerating edge inference of deep neural network (DNN) workloads via joint optimization of wireless and computing resources. WiNPA enables fine-grained integration of wireless communication and edge computing, bridging the research gap between wireless and edge intelligence and significantly improving DNN inference performance. To fully realize its potential, we explore a set of fundamental research issues, including mathematical modeling, optimization, and unified hardware--software platforms. Additionally, key research directions are discussed to guide future development and practical implementation. A case study demonstrates WiNPA's workflow and effectiveness in accelerating DNN inference through simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11150 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WiNPA: Wireless Neural Processing Architecture Xu, Sai Du, Yanan Signal Processing This article presents a wireless neural processing architecture (WiNPA), providing a novel perspective for accelerating edge inference of deep neural network (DNN) workloads via joint optimization of wireless and computing resources. WiNPA enables fine-grained integration of wireless communication and edge computing, bridging the research gap between wireless and edge intelligence and significantly improving DNN inference performance. To fully realize its potential, we explore a set of fundamental research issues, including mathematical modeling, optimization, and unified hardware--software platforms. Additionally, key research directions are discussed to guide future development and practical implementation. A case study demonstrates WiNPA's workflow and effectiveness in accelerating DNN inference through simulations. |
| title | WiNPA: Wireless Neural Processing Architecture |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.11150 |