MDP：具有延迟约束的多维视觉模型剪枝
MDP: Multidimensional Vision Model Pruning with Latency Constraint
AI总结：
提出多维剪枝框架MDP，联合优化多种剪枝粒度并采用先进延迟建模，将剪枝表述为MINLP，在延迟约束下实现CNNs和transformers的最优剪枝结构，显著提升高剪枝率下的速度与精度。
摘要：
当前的结构化剪枝方法面临两个显著局限：（i）它们通常将剪枝限制在通道等更细粒度的层级，使得激进的参数缩减难以实现；（ii）它们严重侧重于参数和FLOP的减少，现有的延迟感知方法经常依赖简单且次优的线性模型，这些模型无法很好地泛化到transformers，其中多个相互作用的维度会影响延迟。在本文中，我们通过引入多维剪枝（MDP）来解决这两个局限，这是一种联合优化各种剪枝粒度（包括通道、query、key、heads、embeddings和blocks）的新颖范式。MDP采用先进的延迟建模技术来准确捕获所有可剪枝维度的延迟变化，在延迟和准确性之间实现最佳平衡。通过将剪枝重新表述为混合整数非线性规划（MINLP），MDP在遵守延迟约束的同时，高效地识别所有可剪枝维度上的最优剪枝结构。这个通用框架支持CNNs和transformers。大量实验表明，MDP显著优于以前的方法，尤其是在高剪枝率下。在ImageNet上，对于ResNet50剪枝，MDP实现了28%的加速，并且比HALP等先前工作提高了+1.4的Top-1准确率。与最新的transformer剪枝方法Isomorphic相比，MDP提供了额外的37%加速，并提高了+0.7的Top-1准确率。
英文摘要：
Current structural pruning methods face two significant limitations: (i) they often limit pruning to finer-grained levels like channels, making aggressive parameter reduction challenging, and (ii) they focus heavily on parameter and FLOP reduction, with existing latency-aware methods frequently relying on simplistic, suboptimal linear models that fail to generalize well to transformers, where multiple interacting dimensions impact latency. In this paper, we address both limitations by introducing Multi-Dimensional Pruning (MDP), a novel paradigm that jointly optimizes across a variety of pruning granularities-including channels, query, key, heads, embeddings, and blocks. MDP employs an advanced latency modeling technique to accurately capture latency variations across all prunable dimensions, achieving an optimal balance between latency and accuracy. By reformulating pruning as a Mixed-Integer Nonlinear Program (MINLP), MDP efficiently identifies the optimal pruned structure across all prunable dimensions while respecting latency constraints. This versatile framework supports both CNNs and transformers. Extensive experiments demonstrate that MDP significantly outperforms previous methods, especially at high pruning ratios. On ImageNet, MDP achieves a 28% speed increase with a +1.4 Top-1 accuracy improvement over prior work like HALP for ResNet50 pruning. Against the latest transformer pruning method, Isomorphic, MDP delivers an additional 37% acceleration with a +0.7 Top-1 accuracy improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
