Designing New Chips for AI-Enabled Spectrum Perception
ECE Assistant Professor Francesco Restuccia, in collaboration with Arjuna Madanayake from Florida International University, Vishal Saxena from the University of Delaware, and Jia Di from the University of Arkansas, was awarded a $2M NSF grant for “FuSe: Deep Learning and Signal Processing using Silicon Photonics and Digital CMOS Circuits for Ultra-Wideband Spectrum Perception.”
Abstract Source: NSF
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.