论文标题
分层修剪，以进行有效的差异化推理感知神经体系结构搜索
Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search
论文作者
论文摘要
我们提出了三种新型的修剪技术，以提高推理意识到的可区分神经结构搜索（DNAS）的成本和结果。首先，我们介绍了Prunode，这是DNA的随机双路构建块，它可以使用O（1）内存和计算复杂性在内部隐藏尺寸进行搜索。其次，我们在搜索过程中提出了一种在超级网的随机层内修剪块的算法。第三，我们描述了一种在搜索过程中修剪不必要的随机层的新颖技术。搜索产生的优化模型称为Prunet，并在Imagenet Top-1图像分类精度的推理潜伏期中为NVIDIA V100建立了新的最先进的Pareto前沿。将Prunet作为骨架还优于COCO对象检测任务的GPUNET和EFIDENENET，相对于平均平均精度（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).
