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Pulse modulation based communication techniques have enabled various Internet of Things (IoT) applications, such as smart meters and automotive systems. However, the existing pulse modulators rely on platform-specific hardware components, leading to limited extensibility and hardware dependency when supporting diverse variants such as Pulse Position Modulation (PPM) and Pulse Amplitude Modulation (PAM). This paper introduces NN-Pulse, an innovative neural networkdefined pulse modulator designed to enhance extensibility and flexibility, ensuring compatibility with multiple pulse modulation schemes. Specifically, NN-Pulse realizes the pulse modulation process using fundamental neural network modules with carefully tailored weights, via the proposed spike neural network (SNN)-based position selection module and transposed convolutional layers for phase modulation. Evaluations show that NNPulse generates PPM and PAM signals with bit error ratios of 0.6% and 0.2%, respectively. Moreover, the time consumption of modulating a pulse symbol via NN-Pulse is only <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.4 \mu \mathrm{s}$</tex>, outperforming traditional methods by 47 times.