论文标题
用于基于框架和基于事件的单一对象本地化的尖峰神经网络
Spiking Neural Networks for Frame-based and Event-based Single Object Localization
论文作者
论文摘要
尖峰神经网络已显示出具有人工神经网络的节能替代品。但是，了解传感器噪声和输入编码对网络活动和性能的影响仍然很困难，对于分类等常见的神经形态视觉基准。因此，我们为使用替代梯度下降训练的单个对象定位提出了一种尖峰神经网络方法，用于基于框架和事件的传感器。我们将方法与类似的人工神经网络进行了比较，并表明我们的模型在准确性，对各种腐败的鲁棒性方面具有竞争力/更好的性能，并且能耗较低。此外，我们研究了神经编码方案对准确性，鲁棒性和能源效率的静态图像的影响。我们的观察结果与以前关于生物成分学习规则的研究重要差异，该规则有助于设计替代梯度训练的体系结构，并就噪声特征和数据编码方法方面的未来神经形态技术中设计优先级。
Spiking neural networks have shown much promise as an energy-efficient alternative to artificial neural networks. However, understanding the impacts of sensor noises and input encodings on the network activity and performance remains difficult with common neuromorphic vision baselines like classification. Therefore, we propose a spiking neural network approach for single object localization trained using surrogate gradient descent, for frame- and event-based sensors. We compare our method with similar artificial neural networks and show that our model has competitive/better performance in accuracy, robustness against various corruptions, and has lower energy consumption. Moreover, we study the impact of neural coding schemes for static images in accuracy, robustness, and energy efficiency. Our observations differ importantly from previous studies on bio-plausible learning rules, which helps in the design of surrogate gradient trained architectures, and offers insight to design priorities in future neuromorphic technologies in terms of noise characteristics and data encoding methods.
