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arXiv · 2209.11785

Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search

Sławomir Kierat·Mateusz Sieniawski·Denys Fridman·Chen-Han Yu·Szymon Migacz·Paweł Morkisz·Alex-Fit Florea
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Abstract

We propose three novel pruning techniques to improve the cost and results of inference-aware Differentiable Neural Architecture Search (DNAS). First, we introduce Prunode, a stochastic bi-path building block for DNAS, which can search over inner hidden dimensions with O(1) memory and compute complexity. Second, we present an algorithm for pruning blocks within a stochastic layer of the SuperNet during the search. Third, we describe a novel technique for pruning unnecessary stochastic layers during the search. The optimized models resulting from the search are called PruNet and establishes a new state-of-the-art Pareto frontier for NVIDIA V100 in terms of inference latency for ImageNet Top-1 image classification accuracy. PruNet as a backbone also outperforms GPUNet and EfficientNet on the COCO object detection task on inference latency relative to mean Average Precision (mAP).

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Sławomir Kierat, Mateusz Sieniawski, Denys Fridman, Chen-Han Yu, Szymon Migacz, Paweł Morkisz, Alex-Fit Florea. 2023-01-05. Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search. https://arxiv.org/abs/2209.11785

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