# Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search Source: [Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:55:03.570Z Authors: S{\\l}awomir Kierat, Mateusz Sieniawski, Denys Fridman, Chen-Han Yu, Szymon Migacz, Pawe{\\l} Morkisz, Alex-Fit Florea Published: 2022-09-23T18:03:54.000Z ## 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). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()