Multi-Dimensional Pruning: Joint Channel, Layer and Block Pruning with Latency Constraint

TL;DR

This paper introduces a multi-dimensional pruning framework that jointly optimizes channels, layers, and blocks under latency constraints, significantly improving model efficiency and accuracy for vision tasks.

Contribution

It presents a novel joint pruning method with a latency-aware model, formulated as a MINLP, enabling aggressive pruning with optimal latency-accuracy trade-offs.

Findings

Outperforms previous pruning methods at high ratios.

Achieves higher FPS and accuracy in classification.

Sets new state-of-the-art in 3D object detection.

Abstract

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.