用AI芯片跑无线基带，首次实现无基带芯片的通信系统
AI-RAN on NPUs: Baseband Processing Without Baseband Chips
- 把通信算法重构为AI计算原语，适配NPU架构
- 在昇腾310B1上实现3.0GHz频段全链路无线传输
- 适合做边缘AI与通信融合的工程师参考
AI-RAN旨在将人工智能与无线接入网工作负载统一在共享计算平台上。尽管该范式此前主要基于图形处理器（GPU）实现，但神经处理单元（NPU）——专为推理优化的AI加速器——是否能支持无线基带处理仍不明确。本文首次给出肯定回答，解决了基带任务与NPU架构的根本不匹配问题。发现矩阵与向量引擎的计算同构性：NPU用于推理的硬件天然覆盖物理层操作。然而，传统基带优化以减少算术运算为目标，而NPU性能依赖最大化引擎利用率，二者存在矛盾。我们通过将通信算法重构为面向AI计算原语，优先保障引擎利用率而非算术量，填补了这一差距。在昇腾310B1边缘NPU上实现完整OFDM收发机，经由USRP X300在3.0 GHz频段完成端到端空口传输验证。
原文摘要 · Abstract (English)
AI-RAN aims to unify artificial intelligence and radio access network workloads on a shared compute substrate. While this paradigm has so far been demonstrated primarily on Graphics Processing Units (GPUs), it remains unclear whether Neural Processing Units (NPUs), which are AI accelerators optimized for inference, can also support wireless baseband processing. Here, we provide the first affirmative answer by resolving the fundamental mismatch between baseband workloads and NPU architecture. A computational isomorphism exists: matrix and vector engines NPUs dedicate to inference inherently cover physical-layer operations. Yet NPU architectures are natively shaped for dense-tensor AI inference, not baseband. This architectural mismatch surfaces as opposing optimization objectives: traditional baseband minimizes arithmetic operations, whereas NPU performance demands maximizing engine utilization. We close this gap by reconstructing communication algorithms onto AI compute primitives, prioritizing engine utilization over arithmetic count. We validate this with a complete OFDM transceiver on an Ascend 310B1 edge NPU, demonstrating end-to-end over-the-air transmission via USRP X300 at 3.0 GHz.
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