Smart sensing by analog/digital hybrid neural networks

In this dissertation, the design, computer simulations, experimental evaluations, and theoretical analyses of new sensing schemes were systematically carried out. A novel power-efficient and low-complexity analog sensor design, which is the foundation of this PhD thesis, was validated through comprehensive simulations and hardware experiments. Additionally, a sensor in analog/digital hybrid single neuron and neural network prototype was developed for practical monitoring scenarios, progressing to ECG signal compression in the analog domain for health monitoring applications. The research further explores the integration of analog/digital hybrid neural networks for UAV applications, leveraging the strengths of both domains. Furthermore, the feasibility of applying analog/digital hybrid circuits in Spiking Neural Networks (SNNs) was explored, aiming to offload complex computations from the digital part to maximize computational efficiency while leveraging the energy-saving benefits of analog design. We validated the proposed methods using computer vision datasets, including digits, text, and objects with varying levels of recognition difficulty, to analyze the feasibility of the approach. Theoretical analyses and detailed simulations provide a deep understanding of signal recovery performance and parameter optimization. This dissertation is rooted in low-power analog sensor innovation and extends to persistent wireless sensing, biosensing applications, and hybrid neural network implementations.