Abstract
Event cameras offer low-power visual sensing capabilities ideal for edge-device applications. However, their high event rate, driven by high temporal details, can be restrictive in terms of bandwidth and computational resources. In edge AI applications, determining the minimum amount of events for specific tasks can allow reducing the event rate to improve bandwidth, memory, and processing efficiency. In this paper, we study the effect of event subsampling on the accuracy of event data classification using convolutional neural network (CNN) models. Surprisingly, across various datasets, the number of events per video can be reduced by an order of magnitude with little drop in accuracy, revealing the extent to which we can push the boundaries in accuracy vs. event rate trade-off. Additionally, we also find that lower classification accuracy in high subsampling rates is not solely attributable to information loss due to the subsampling of the events, but that the training of CNNs can be challenging in highly subsampled scenarios, where the sensitivity to hyperparameters increases. We quantify training instability across multiple event-based classification datasets using a novel metric for evaluating the hyperparameter sensitivity of CNNs in different subsampling settings. Finally, we analyze the weight gradients of the network to gain insight into this instability.
Abstract (translated)
活动相机具有低功耗的视觉感知能力，特别适用于边缘设备应用。然而，由于其高事件率，驱动因素是高时间细节，在带宽和计算资源方面可能会有所限制。在边缘人工智能应用中，确定特定任务所需的最低事件量可以降低事件率，提高带宽、内存和处理效率。在本文中，我们研究了事件子采样对使用卷积神经网络（CNN）模型进行事件数据分类的准确性的影响。令人惊讶的是，在各种数据集中，每个视频的事件数量可以降低一个数量级，而准确度几乎没有下降，揭示了我们在准确性和事件率之间的权衡程度。此外，我们还发现，在高度子采样的情况下，较低的分类准确性不仅仅是因为事件子采样导致的，而且是因为在高度子采样情况下训练CNN可能具有挑战性，参数对超参数的敏感性增加。我们使用一种新的指标，评估不同子采样设置中CNN的参数敏感性，来量化训练不稳定。最后，我们分析了网络的权重梯度，以深入了解这种不稳定性。
URL
https://arxiv.org/abs/2409.08953
