Neural Architecture Search (NAS) has become the de-facto approach for designing accurate and efficient networks for edge devices. Since models are typically quantized for edge deployment, recent work has investigated quantization-aware NAS (QA-NAS) to search for highly accurate and efficient quantized models. However, existing QA-NAS approaches, particularly few-bit mixed-precision (FB-MP) methods, do not scale to larger tasks. Consequently, QA-NAS has mostly been limited to low-scale tasks and tiny networks. In this work, we present an approach to enable QA-NAS (INT8 and FB-MP) on large-scale tasks by leveraging the block-wise formulation introduced by block-wise NAS. We demonstrate strong results for the semantic segmentation task on the Cityscapes dataset, finding FB-MP models 33% smaller and INT8 models 17.6% faster than DeepLabV3 (INT8) without compromising task performance.

使用基于块的NAS方法实现在大规模任务上的量化感知NAS (INT8和FB-MP)，并在Cityscapes数据集上展示了显著的结果，找到了比DeepLabV3 (INT8)模型小33%， INT8模型快17.6%的FB-MP模型，而不影响任务性能。

在边缘上的高效深度学习：扩展量化感知的神经架构搜索