3 accepted papers
Vision Transformers (ViTs) have achieved remarkable success across various vision tasks, yet their deployment is often hindered by prohibitive computational costs. While structured weight pruning and token compression have emerged as promising solutions, they suffer from prolonged retraining times a…
Recent GPUs leverage Winograd convolution and structured pruning to significantly accelerate inference. First, Winograd convolution is theoretically 2.25x faster than standard convolution. Second, structured pruning reduces inference time without additional overhead as the pruning ratio increases. H…
Depth-wise Separable Convolution (DSConv) has a powerful representation even with fewer parameters and computation, leading to its adoption by almost all of the state-of-the-art CNN models. DSConv models are already compact making it hard to apply pruning, and there are few previous pruning techni…