As we push the boundaries of performance in various vision tasks, the models
grow in size correspondingly. To keep up with this growth, we need very
aggressive pruning techniques for efficient inference and deployment on edge
devices. Existing pruning approaches are limited to channel pruning and
struggle with aggressive parameter reductions. In this paper, we propose a
novel multi-dimensional pruning framework that jointly optimizes pruning across
channels, layers, and blocks while adhering to latency constraints. We develop
a latency modeling technique that accurately captures model-wide latency
variations during pruning, which is crucial for achieving an optimal
latency-accuracy trade-offs at high pruning ratio. We reformulate pruning as a
Mixed-Integer Nonlinear Program (MINLP) to efficiently determine the optimal
pruned structure with only a single pass. Our extensive results demonstrate
substantial improvements over previous methods, particularly at large pruning
ratios. In classification, our method significantly outperforms prior art HALP
with a Top-1 accuracy of 70.0(v.s. 68.6) and an FPS of 5262 im/s(v.s. 4101
im/s). In 3D object detection, we establish a new state-of-the-art by pruning
StreamPETR at a 45% pruning ratio, achieving higher FPS (37.3 vs. 31.7) and mAP
(0.451 vs. 0.449) than the dense baseline.

提出了一种新的多维修剪框架，可以同时优化通道、层和块的修剪，并满足延迟约束，通过将修剪重新构建为混合整数非线性规划 (MINLP)，能够高效地确定最佳修剪结构。该方法在大规模修剪比例下取得了显著的改进。