Event cameras play a crucial role in the visual field; however, they are susceptible to noise. Traditional denoising algorithms for event cameras often struggle to balance accuracy and speed. To address this issue, this paper proposes an algorithm named Event Denoising Based on Iterative Tree-Structured Information Aggregation (EDIST). Specifically, the proposed method first establishes connections in event streams using a spatiotemporal window to extract Relation Tree. Then, a pruning algorithm is employed to streamline the subsequent information aggregation process, followed by a multi-stage convolution module designed to process the Relation Tree and obtain aggregated features. Finally, these aggregated features are fed into a classification module to determine whether an event is noise. During the inference stage, an information storage reuse module is designed to enable iterative execution, thereby enhancing inference speed. Experimental results demonstrate that the proposed algorithm outperforms existing methods in both denoising accuracy and inference speed on the DVSNOISE20 dataset.

As neural networks grow in size and complexity, inference speeds decline. To combat this, one of the most effective compression techniques -- channel pruning -- removes channels from weights. However, for multi-branch segments of a model, channel removal can introduce inference-time memory copies. In turn, these copies increase inference latency -- so much so that the pruned model can be slower than the unpruned model. As a workaround, pruners conventionally constrain certain channels to be pruned together. This fully eliminates memory copies but, as we show, significantly impairs accuracy. We now have a dilemma: Remove constraints but increase latency, or add constraints and impair accuracy. In response, our insight is to reorder channels at export time, (1) reducing latency by reducing memory copies and (2) improving accuracy by removing constraints. Using this insight, we design a generic algorithm UPSCALE to prune models with any pruning pattern. By removing constraints from existing pruners, we improve ImageNet accuracy for post-training pruned models by 2.1 points on average -- benefiting DenseNet (+16.9), EfficientNetV2 (+7.9), and ResNet (+6.2). Furthermore, by reordering channels, UPSCALE improves inference speeds by up to 2x over a baseline export.