Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is inevitable and must be dealt with. Algorithms and models operating on radar data are required to run the early processing steps on specialized radar sensor hardware. This specialized hardware typically has strict resource-constraints, i.e. a low memory capacity and low computational power. Convolutional Neural Network (CNN)-based approaches for denoising and interference mitigation yield promising results for radar processing in terms of performance. Regarding resource-constraints, however, CNNs typically exceed the hardware's capacities by far. In this paper we investigate quantization techniques for CNN-based denoising and interference mitigation of radar signals. We analyze the quantization of (i) weights and (ii) activations of different CNN-based model architectures. This quantization results in reduced memory requirements for model storage and during inference. We compare models with fixed and learned bit-widths and contrast two different methodologies for training quantized CNNs, i.e. the straight-through gradient estimator and training distributions over discrete weights. We illustrate the importance of structurally small real-valued base models for quantization and show that learned bit-widths yield the smallest models. We achieve a memory reduction of around 80% compared to the real-valued baseline. Due to practical reasons, however, we recommend the use of 8 bits for weights and activations, which results in models that require only 0.2 megabytes of memory.

中文翻译：

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用于汽车雷达干扰缓解的资源高效深度神经网络

雷达传感器对于驾驶员辅助系统和自动驾驶汽车的环境感知至关重要。随着雷达传感器数量的增加和迄今为止未受管制的汽车雷达频段，相互干扰是不可避免的，必须加以处理。需要对雷达数据进行运算的算法和模型才能在专用雷达传感器硬件上运行早期处理步骤。这种专用硬件通常具有严格的资源约束，即低内存容量和低计算能力。基于卷积神经网络 (CNN) 的降噪和干扰抑制方法在性能方面为雷达处理带来了有希望的结果。然而，就资源限制而言，CNN 通常远远超出硬件的能力。在本文中，我们研究了基于 CNN 的雷达信号去噪和干扰抑制的量化技术。我们分析了不同的基于 CNN 的模型架构的 (i) 权重和 (ii) 激活的量化。这种量化导致模型存储和推理期间的内存需求减少。我们比较了具有固定和学习位宽的模型，并对比了两种不同的训练量化 CNN 的方法，即直通梯度估计器和离散权重上的训练分布。我们说明了结构上小的实值基础模型对于量化的重要性，并表明学习到的位宽会产生最小的模型。与实值基线相比，我们实现了大约 80% 的内存减少。但由于实际原因，