Project Grant 2551977

The National Science Foundation Division of Information and Intelligent Systems awarded Syracuse University $350,000 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop algorithm-design-technology co-optimization frameworks for next-generation self-evolving implantable medical devices.

The project establishes a cross-layer co-design framework jointly optimizing applications, learning algorithms, hardware architectures, and system-level operation to enable implantable devices—including pacemakers, implantable cardioverter defibrillators, deep brain stimulators, cochlear implants, and sleep apnea implants—to automatically adapt to individual patients' changing physiological needs while operating within strict constraints on size, power, and battery life. Research is organized into five integrated tasks across two phases. The first phase explores device requirements, develops lightweight state-space models for physiological signal analysis, introduces uncertainty-aware safety mechanisms, and designs energy-efficient in-memory-computing architectures. The second phase advances adaptive model updating, uncertainty-triggered evolution, hardware reconfiguration, and algorithm-architecture co-evolution, investigating structured state-space models, adaptive quantization, Bayesian confidence estimation, processing-in-memory architectures, fault tolerance techniques, and reinforcement learning-based optimization.

Performance occurs at Syracuse, New York through July 31, 2030. This is a Project Grant, an assistance type providing support for discrete, specified research and development efforts.