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: 2329013
• 负责人: Francesco Restuccia
• 金额: $ 48.68万
• 依托单位: Northeastern University
• 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=2329013&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。它是最珍贵和最稀缺的自然资源之一。未来的技术应用，如数字双胞胎、智能汽车和增强现实，要求随时随地都能实现每秒1000次的无线连接。这些需求需要有效的机制来保证高效和安全的RF频谱接入。现有的方法使用简单的技术，可以检测用户在频谱中的存在，但不能感测“谁，何时以及如何”使用频谱。尽管如此，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“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规模感知带宽。为了解决这个问题，该项目将利用混合集成方法，其中光子和电子小芯片或小芯片将协同组合，以促进整个RF频谱上的AI/ML RF感知。该项目的教育部分将通过结合光子和电子芯片设计、AI/ML和无线技术技能的培训来解决美国半导体劳动力短缺的问题。FuSe团队将在半导体、芯片设计和无线通信等主题上指导代表性不足的女性和少数民族。利用基于人工智能的项目与高中生进行接触，将有助于建立一个学生管道，以攻读半导体和计算专业的工程学位。一个关键的教育重点是通过对模拟和数字IC设计课程的全面改造，快速培训学生掌握新的FinFET节点。为了实现人工智能频谱感知，这个融合FuSe项目将把光子集成电路（PIC）与混合信号和节能异步数字芯片共同集成，以实现实时宽带RF感知。PIC前端将允许超过24 GHz带宽的RF频谱处理和信道化。混合信号IC将PIC的输出与数字AI加速器小芯片连接。该团队将使用快速卷积神经网络创建用于调制识别、频谱感知和无线物联网（IoT）设备或特定RF硬件前端检测的AI/ML算法。PI将采用速度/功率高效的RF处理核心的高级合成（HLS），以实现实时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，并将使用权重量化、压缩处理、量化感知再训练、信号流图修剪和功率/面积优化的数字计算电路来实现低复杂性和快速吞吐量。该团队将把数字核心合成为异步数字小芯片。最后，光子和电子芯片将采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，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。