In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition and natural language understanding. One of the significant contributors to its success is the proliferation of end devices that acted as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference is the main challenge. Usually, central cloud servers are used for the computation, but it opens up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers. Moreover, the confluence point of DL and edge has given rise to edge intelligence (EI). This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important in mission-critical applications, e.g., health care. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism and split learning, which facilitate DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented.

我们探索如何利用神经辐射字段（NERF）来构建来自大型视觉捕获的跨越建筑物的交互式3D环境，甚至主要从遗工数据收集的多个城市块。与传统上NERFS传统评估的单个对象场景相比，该设置造成了多种挑战，包括（1）需要包含具有不同照明条件的数千个图像的需要，所有这些都是仅捕获场景的一个小子集（2 ）通过在单个GPU上可以胆怯地培训的内容高度验证，（3）预先强化所有相关信息的任意大量可能的观点（作为实时NERF渲染器通常做的所有相关信息）。为了解决这些挑战，我们首先分析了大规模场景的可见度统计数据，激励了稀疏网络结构，其中参数专门从事场景的不同区域。我们介绍一个简单的几何聚类算法，将训练图像（或相当像素）分区为可以并行培训的不同NERF子模块。我们在跨越6K和Urbanscene3D数据集中采取的场景中评估我们的方法以及对我们自己的无人机镜头以及3倍培训加速，同时平均提高PSNR以上超过11％。我们随后对Mega-Nerf的顶部进行了近期NERF快速渲染器的实证评估，并引入了一种利用时间一致性的新方法。我们的技术通过传统的NERF渲染实现了40倍的加速，同时在PSNR质量下剩余0.5 dB，超过现有快速渲染器的保真度。