FuSe-TG: Cross-layer Co-Design for Self-Evolving Implantable Devices

Medical implantable devices have been widely adopted in many different parts of the body for various applications in the past six decades. They are used to self-manage diseases before or after diseases occur based on the sensed physiological signals. However, the temporal patterns of the physiological signals vary greatly for different individuals (i.e., inter-patient variability). A hardened detection algorithm will not be adequate for all patients to accurately detect the targeting disease (or events) with such variability. The current implantable devices, once implanted, only allow the parameter values of the detection algorithms to be iteratively tuned by doctors. To address the challenges, this project aims to develop the next generation implantable devices that are self-evolvable, allowing dynamic, continuous, and automatic adjustment of both the detection algorithms and the hardware configurations based on individual patient’s circumstances (e.g., data features, ambient environments, device conditions) throughout the devices’ lifetime. A self-evolvable design paradigm with software-hardware co-exploration will be studied to enable low-power, low-overhead, and on-device model personalization and inference for implantable devices. The success of this project will lead to higher detection accuracy, longer implantable device lifetime, and higher quality of care for patients with implantable devices. More importantly, this project will develop a novel cross-layer co-exploration design paradigm for different types of implantable devices to conduct personalized disease management. The education impacts of the proposed research include the integration of various educational activities based on the resources available to the PIs such as DAC System Design Contest and TinyML Design Contest at ICCAD; outreach for local K-12 students through ND’s CS curriculum for K-12 students in Indiana and an in-person webinar named KidSemicon; an online social forum that allows students, researchers, and engineers to communicate, learn, and share knowledge in the domain of reconfigurable semiconductor technologies; undergraduate research with emphasis on minority participation, and course integration of the research outcomes. 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.