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: 2329015
• 负责人: Vishal Saxena
• 金额: $ 49万
• 依托单位: University of Delaware
• 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=2329015&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）频谱编织了无线通信的结构。它是最珍贵，最稀缺的自然资源之一。明天的技术应用程序，例如数字双胞胎，智能车辆和增强现实的需求千兆位无线连接到各地。这种要求要求有效的机制来保证有效和安全的RF光谱访问。现有方法使用简单的技术可以检测用户在频谱中的存在，但无法感觉到所使用的频谱的“谁，何时，以及如何”。尽管如此，人们承诺，包括但不限于机器学习（ML）技术在内的新兴人工智能（AI）方法可以实现“ RF感知”。使用AI算法进行RF感知的一个棘手问题是无法处理频谱的大量感知带宽。为了解决这个问题，该项目将利用混合整合方法，在该方法中，光子和电子小芯片或芯片将在整个RF频谱中协同组合以支持支持AI/ML的RF感知。该项目的教育组成部分将通过对光子和电子芯片设计，AI/ML和无线技术技能的培训结合使用美国半导体劳动力的死亡。保险丝团队将指导诸如半导体，芯片设计和无线沟通等主题的妇女和少数民族。使用基于AI的项目向高中生的宣传将有助于建立一条学生，以攻读专注于半导体和计算的工程学位。一个关键的教育重点是通过对模拟和数字IC设计课程进行完整的改建，对学生进行新的FinFET节点的快速培训。 PI将在合作者之间共享发达的教育和培训材料，并使它们在线提供。为了实现AI-a-Spectrum Sensing Sensing，这个收敛的保险丝项目将与光子集成电路（PIC）协调，并具有混合信号和能效的异步数字数字，以实现实时的宽带RF RF感知。 PIC前端将允许RF频谱处理和通道24 GHz超过24 GHz的带宽。混合信号IC将与数字AI加速器chiplets接口PIC的输出。该团队将使用快速的卷积神经网络创建用于调制识别，频谱感测和检测的AI/ML算法，以识别无线互联网设备（IoT）设备或特定的RF硬件前端。 PI将采用实时AI/ML算法实现的速度/功率RF处理内核的高级合成（HLS）。这些HLS原型将被定制为最小芯片面积和功耗，并使用权重量化，复杂的处理，量化感知的重新处理，信号流图修剪以及功率/区域优化的数字计算电路实现低复杂性和快速吞吐量。该团队将将数字核心合成为异步数字芯片。最后，光子学和电子芯片将通过包括基于FinFET的CMOS流程在内的最先进的商业铸造厂进行逐渐变细和制造，然后打包进行测试和评估。该奖项反映了NSF的法定任务，并被认为是使用该基金会的知识分子的知识和广泛影响的评估来评估CRITERIA CRITERIA的评估。