AI 中文总结
针对无线通信系统硬件优化需求，提出与仿真测试环境关联的可扩展Python-based RTL生成器，可适配流片、FPGA等平台，在AI辅助设计工作流中具应用潜力。
AI 中文摘要
硬件优化对高效无线通信系统的设计至关重要。无线通信硬件通常消耗整个系统的大部分功率预算，其中大量功率用于降低各类噪声的电路，尤其是模拟前端的噪声。单芯片微型 mote（SCuM）采用无晶振的无线电架构，在设计中权衡取舍，优先考虑功耗而非噪声性能，同时保持与IEEE 802.15.4、蓝牙低功耗等流行物联网（IoT）协议的标准兼容性。在SCuM的持续开发中，我们认识到已开发的数字基带硬件可与芯片架构进行更紧密的优化。本文提出一种可扩展的基于Python的RTL生成器，该生成器与仿真和测试环境紧密关联，此方法为在不同硬件平台（如流片和FPGA实现）上使用提供了灵活性，且在AI辅助设计工作流中具有应用前景。
英文摘要
Hardware optimization is critical in the design of efficient wireless communication systems. Wireless communication hardware often consumes a significant fraction of the total system's power budget, with much of this power used in circuits that reduce various types of noise, particularly in the analog front end. The Single-Chip Micro Mote, or SCuM, uses a crystal-free radio architecture and makes design trade-offs that favor power consumption over noise performance while maintaining standards compatibility with popular Internet-of-Things (IoT) protocols such as IEEE 802.15.4 and Bluetooth Low Energy. In the continued development of SCuM, we recognize that the digital baseband hardware developed can be more closely optimized with the architecture of the chip. In this paper, we present an extensible Python-based RTL generator that is closely linked to simulation and testing environments. This approach provides flexibility for use on different hardware platforms, such as tape-outs and FPGA implementations, and has promise in AI-assisted design workflows.
