AI 中文总结
本文提出面向OCUDU 5G物理层与O-RAN前传的GPU驻留CUDA加速架构，在NVIDIA DGX Spark平台上实现多类5G链路处理的显著加速，且性能与CPU基准偏差极小，还支持AI-RAN相关研究运行。
AI 中文摘要
本文介绍DeepSig基于CUDA的OCUDU物理层与O-RAN前传链路加速后端，通过与底层加速机制基本无关的加速接口实现集成。该设计在保留现有工厂、资源网格接口、PRACH缓冲器接口及信道处理器的同时，加速PDSCH、PUSCH、SRS、PRACH、split-8低物理层变换，以及O-RAN前传（O-FH）IQ压缩/解压。当平台与射频拆分允许时，CUDA可见网格、设备侧软比特缓冲器、流事件、固定暂存缓冲器及托管内存策略可使数据驻留在加速器上。在搭载GB10 GPU与ARM CPU主机的NVIDIA DGX Spark平台上，将CPU基准固定在高容量核心的代表性测量结果显示，与生产级CPU路径相比，PUSCH加速最高达10.3倍、PDSCH加速2.7倍、采用时隙形批处理与分散映射零拷贝的split-8低物理层接收加速19.7倍、O-FH BFP12解压加速91.4倍、PRACH检测器加速28.8倍；在测试的PUSCH扫描中，CPU与GPU的10% BLER阈值偏差在0.064 dB以内。该驻留管线还为AI-RAN提供执行载体，使机器学习信道估计、神经接收机及AI原生空中接口研究可与符合标准的基带内核协同运行。
英文摘要
This paper describes DeepSig's CUDA-based acceleration backend for the OCUDU physical layer and O-RAN fronthaul path, integrated through acceleration interfaces that are largely independent of the underlying acceleration mechanism. The design accelerates PDSCH, PUSCH, SRS, PRACH, split-8 lower-PHY transforms, and O-RAN fronthaul (O-FH) IQ compression/decompression while preserving existing factories, resource-grid interfaces, PRACH-buffer interfaces, and channel processors. CUDA-visible grids, device-side softbit buffers, stream events, pinned staging buffers, and managed-memory policies keep data resident on the accelerator when the platform and radio split permit it. On an NVIDIA DGX Spark platform with a GB10 GPU and ARM CPU host, representative measurements with CPU baselines pinned to high-capacity cores show up to 10.3x PUSCH speedup, 2.7x PDSCH speedup, 19.7x split-8 low-PHY RX speedup with slot-shaped batching and scattered mapped zero-copy, 91.4x O-FH BFP12 decompression speedup, and 28.8x PRACH detector speedup against the production CPU path, with CPU and GPU 10% BLER thresholds agreeing to within 0.064 dB in the tested PUSCH sweeps. The same resident pipeline provides an execution substrate for AI-RAN, allowing machine-learned channel estimation, neural receivers, and AI-native air-interface research to run beside standards-compliant baseband kernels.
Comments11 pages, 4 figures, pre-publication
