Collaborative Research: FuSe: Deep Learning and Signal Processing using Silicon Photonics and Digital CMOS Circuits for Ultra-Wideband Spectrum Perception

合作研究：FuSe：利用硅光子学和数字 CMOS 电路实现超宽带频谱感知的深度学习和信号处理

• 批准号: 2329014
• 负责人: Jia Di
• 金额: $ 39.98万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329014&HistoricalAwards=false
• 关键词: Collaborative; Research; FuSe; Deep; Learning

The radio frequency (RF) spectrum weaves the very fabric of wireless communications. And it is among the most precious and scarcest of natural resources. Tomorrow’s tech applications such as digital twins, smart vehicles, and augmented reality demand Gigabit-per-second wireless connectivity everywhere all the time. Such demands call for effective mechanisms to guarantee efficient and secure RF spectrum access. Existing methods use simple techniques that can detect users' presence in the spectrum but cannot sense the “who, when, and how” of the spectrum being utilized. Nonetheless, emerging artificial intelligence (AI) methods including but not limited to machine learning (ML) techniques are promising for achieving “RF perception.” A thorny problem in using AI algorithms for RF perception is the inability to process the massive sensed bandwidth of the spectrum. To solve this problem, this project will leverage a hybrid integration approach, where photonic and electronic small chips, or chiplets, will be synergistically combined to facilitate AI/ML-enabled RF perception over the entire RF spectrum. The education component of the project will address the dearth in the US-based semiconductor workforce through a combination of training on photonic and electronic chip design, AI/ML, and wireless technology skills. The FuSe team will mentor women and minorities who are underrepresented, in topics such as semiconductors, chip design, and wireless communication. Outreach to high-school students using AI-based projects will help build a pipeline of students to pursue engineering degrees focusing on semiconductors and computing. A critical educational emphasis is to fast-track training of students on newer FinFET nodes through a complete revamp of analog and digital IC design courses. The PIs will share the developed education and training material amongst the collaborators and make them available online.To achieve AI-enabled spectrum sensing, this convergent FuSe project will co-integrate a photonic integrated circuit (PIC) with mixed-signal and energy-efficient asynchronous digital chiplets to realize real-time wideband RF perception. The PIC front-end will allow RF spectrum processing and channelization of over 24 GHz of bandwidth. The mixed-signal IC will interface the PIC’s output with digital AI accelerator chiplets. The team will create AI/ML algorithms for modulation recognition, spectrum sensing, and detection of wireless internet-of-things (IoT) devices or specific RF hardware front-ends using fast convolutional neural networks. PIs will employ high-level synthesis (HLS) of speed/power-efficient RF processing cores for real-time AI/ML algorithm implementation. These HLS prototypes will be custom optimized for minimum chip area and power consumption and will achieve low complexity and fast throughput using weight quantization, compressive processing, quantization-aware retraining, signal flow graph pruning, and power/area-optimized digital computing circuits. The team will synthesize the digital cores as asynchronous digital chiplets. Finally, the photonics and electronic chiplets will be taped-out and fabricated using state-of-the-art commercial foundries including the FinFET-based CMOS process, and then packaged for testing and evaluation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

射频 (RF) 频谱构成了无线通信的基础，它是最宝贵和最稀缺的自然资源之一，数字孪生、智能汽车和增强现实等未来的技术应用需要每秒千兆位的无线连接。这些需求需要有效的机制来保证高效和安全的射频频谱访问，现有方法使用简单的技术，可以检测用户在频谱中的存在，但无法感知频谱的“谁、何时以及如何”。被利用。新兴的人工智能（AI）方法，包括但不限于机器学习（ML）技术，有望实现“射频感知”。使用人工智能算法进行射频感知的一个棘手问题是无法处理大量感知到的频谱带宽。为了解决这个问题，该项目将利用混合集成方法，将光子和电子小芯片或小芯片协同组合，以促进整个射频频谱上支持人工智能/机器学习的射频感知。该项目的教育部分将。解决FuSe 团队将通过结合光子和电子芯片设计、人工智能/机器学习和无线技术技能培训，针对半导体劳动力短缺的问题，在半导体、芯片设计、使用基于人工智能的项目向高中生进行推广将有助于建立学生攻读半导体和计算工程学位的渠道，一个关键的教育重点是通过一个新的 FinFET 节点快速培训学生。模拟的彻底改造PI 将在合作者之间共享开发的教育和培训材料，并将其在线提供。为了实现人工智能支持的频谱感测，这个融合 FuSe 项目将与混合光子集成电路 (PIC) 共同集成。 -信号和高能效异步数字芯片，可实现实时宽带射频感知。混合信号 IC 将实现射频频谱处理和超过 24 GHz 带宽的通道化。该团队将使用快速卷积神经网络创建 AI/ML 算法，用于调制识别、频谱感知以及无线物联网 (IoT) 设备或特定 RF 硬件前端的检测。 PI 将采用速度/节能射频处理核心的高级综合 (HLS) 来实现实时 AI/ML 算法，这些 HLS 原型将针对最小芯片面积和功耗进行定制优化，并实现低复杂性。以及使用权重量化、压缩处理、量化感知再训练、信号流图修剪和功率/面积优化数字计算电路的快速吞吐量，该团队将把数字核心合成为异步数字小芯片。使用最先进的商业代工厂（包括基于 FinFET 的 CMOS 工艺）进行流片和制造，然后进行封装以进行测试和评估。该奖项反映了 NSF 的法定使命通过使用基金会的智力价值和更广泛的影响审查标准进行评估，并被认为值得支持。