arXiv subjects

Fanfu Wu

Publications and source records attributed to Fanfu Wu.

2 recordsLinked to original sources

arXiv2026

Memristor-Based Pulse Width Modulation Circuit for Power Converters with Programmable Frequencies

Memristors can achieve up to a 1000-times reduction in energy consumption in neuromorphic and in-memory computing, but their integration into power converter control remains incomplete. Existing implementations either convert memristor outputs into digital signals for DSP-based pulse-width modulation (PWM), reintroducing computational overhead, or use analog PWM circuits with limited programmability. Since PWM generation is the final essential stage of power converter control, a programmable memristor-based PWM circuit is needed to fully realize the energy-efficiency benefits of memristive computing. This paper presents a programmable memristor-based PWM circuit operating at approximately two hundred kilohertz for switching power converters. The design combines digital-controller programmability with the low power consumption of analog PWM generators. Experimental validation using commercially available memristors achieves a programmable switching frequency from 144.7 kHz to 204.2 kHz. Compared with a commonly used DSP implementation in power converter control, the proposed design achieves a 92% reduction in power consumption.

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arXiv2026

Ultra-Low-Energy Open-Circuit Fault Diagnosis for Three-Phase Inverters

Embedded fault diagnosis in three-phase inverters must satisfy the sub-watt power budget of converter control hardware, but conventional convolutional neural network (CNN)-based methods require dense multiply-accumulate operations and impose substantial inference energy. This work proposes an event-driven neuromorphic framework for energy-efficient open-circuit (OC) fault diagnosis. A CNN trained on current-vector trajectory matrices is converted into a spiking neural network (SNN) and evaluated using the NengoLoihi framework with Loihi-based neuromorphic energy estimation. By exploiting the sparse structure of trajectory matrices, the SNN activates computation only in informative regions instead of processing the full feature map densely. Experiments on a three-phase inverter platform show that the proposed method achieves 11 microjoules per diagnosis, corresponding to a 382 times inference-energy reduction compared with a GPU-based CNN, while maintaining 100% diagnostic accuracy. Robustness is further validated under unbalanced loading, current amplitude step changes, and injected measurement noise.

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