Structural pruning can simplify network architecture and improve inference
speed. We propose Hardware-Aware Latency Pruning (HALP) that formulates
structural pruning as a global resource allocation optimization problem, aiming
at maximizing the accuracy while constraining latency under a predefined
budget. For filter importance ranking, HALP leverages latency lookup table to
track latency reduction potential and global saliency score to gauge accuracy
drop. Both metrics can be evaluated very efficiently during pruning, allowing
us to reformulate global structural pruning under a reward maximization problem
given target constraint. This makes the problem solvable via our augmented
knapsack solver, enabling HALP to surpass prior work in pruning efficacy and
accuracy-efficiency trade-off. We examine HALP on both classification and
detection tasks, over varying networks, on ImageNet and VOC datasets. In
particular, for ResNet-50/-101 pruning on ImageNet, HALP improves network
throughput by $1.60\times$/$1.90\times$ with $+0.3\%$/$-0.2\%$ top-1 accuracy
changes, respectively. For SSD pruning on VOC, HALP improves throughput by
$1.94\times$ with only a $0.56$ mAP drop. HALP consistently outperforms prior
art, sometimes by large margins.

通过硬件感知延迟剪枝（HALP）将结构剪枝作为全局资源分配优化问题，同时控制延迟在预定义的预算下最大化准确性。 HALP 使用延迟查找表来跟踪延迟减少潜力，使用全局显着性得分来评估准确度降低，并可以在剪枝期间非常高效地评估这两个指标，使我们能够在目标约束下重构全局结构剪枝问题，从而通过我们的增强型背包求解器解决问题，从而使 HALP 在剪枝效果和准确性效率平衡方面超越以前的工作。