Designing Bayesian based Adaptive Resource Constrained Hardware Algorithms for Next Generation of Embedded Systems

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Novel learning approaches from deep learning (DL) community are introduced to allow DL agents to adapt and continually learn in the same or new environments to mimic human like learning such as transfer learning, few shot learning, and continual learning. Despite these approaches there are still limitations that must be addressed to enable seamless and adaptable AI on resource constrained sensing platforms. Firstly, the existing solutions focus is often dominated by accuracy and lacks attention to other performance measures such as memory, CPU, and energy consumption which is critical to low-power resource-constrained devices. Secondly, the current work does not account for the uncertainties in the learning process and focus on inference on the device. We would like to go beyond inference and take on more challenging pursuit of learning on the device. The overall goal of this research project would be to investigate efficient and reliable on-device learning solutions for resource-constrained devices such as MCUs (microcontroller units) and FPGAs. Specifically, the aims of the project would include exploring the performance of different Bayesian continual learning approaches for various sensing tasks to find existing issues, investigate and design novel ML algorithms for Bayesian continual learning by solving these issues and propose system-based optimization techniques.