Edge computing aims to enable edge devices, such as IoT devices, to process
data locally instead of relying on the cloud. However, deep learning techniques
like computer vision and natural language processing can be computationally
expensive and memory-intensive. Creating manual architectures specialized for
each device is infeasible due to their varying memory and computational
constraints. To address these concerns, we automate the construction of
task-specific deep learning architectures optimized for device constraints
through Neural Architecture Search (NAS). We present DCA-NAS, a principled
method of fast neural network architecture search that incorporates edge-device
constraints such as model size and floating-point operations. It incorporates
weight sharing and channel bottleneck techniques to speed up the search time.
Based on our experiments, we see that DCA-NAS outperforms manual architectures
for similar sized models and is comparable to popular mobile architectures on
various image classification datasets like CIFAR-10, CIFAR-100, and
Imagenet-1k. Experiments with search spaces -- DARTS and NAS-Bench-201 show the
generalization capabilities of DCA-NAS. On further evaluating our approach on
Hardware-NAS-Bench, device-specific architectures with low inference latency
and state-of-the-art performance were discovered.

边缘计算旨在使边缘设备（如物联网设备）能够在本地处理数据，而不是依赖云端。本文提出了一种基于 DCA-NAS 的快速神经网络架构搜索方法，该方法结合了边缘设备的约束条件，如模型大小和浮点运算，通过权重共享和通道瓶颈技术加快搜索速度，并发现了具有低推断延迟和最先进性能的特定设备架构。