Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search
This work addresses efficiency bottlenecks in neural architecture search for researchers and practitioners, though it is incremental as it builds upon existing DNAS methods.
The authors tackled the problem of high computational cost in Differentiable Neural Architecture Search (DNAS) by proposing three novel pruning techniques, resulting in PruNet models that achieve a new state-of-the-art Pareto frontier for inference latency on ImageNet (e.g., NVIDIA V100) and outperform GPUNet and EfficientNet on COCO object detection in terms of latency relative to mAP.
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).