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).
翻译:我们提出了三种新颖的修剪技术,以提高推断觉察到差异神经结构搜索的成本和结果。 首先,我们引入了Prunode,这是DNAS的一个双路径构件,可以用O(1)内存和计算复杂度来搜索内部隐藏的维度。第二,我们在搜索过程中为SuperNet的蒸馏层内修剪块提供了一种算法。第三,我们描述了在搜索过程中修剪不必要随机层的新型技术。搜索产生的优化模型称为PruNet,并在图像网络Top-1图像分类精度的推论值方面为 NVIDIA V100 建立了一个新的最先进的Pareto边框。PruNet作为主干网,也超越了CoCO物体探测任务中与平均精度相对的推测 Latenity( mAP ) 。