Neural Radiance Field~(NeRF) achieves extremely high quality in object-scaled
and indoor scene reconstruction. However, there exist some challenges when
reconstructing large-scale scenes. MLP-based NeRFs suffer from limited network
capacity, while volume-based NeRFs are heavily memory-consuming when the scene
resolution increases. Recent approaches propose to geographically partition the
scene and learn each sub-region using an individual NeRF. Such partitioning
strategies help volume-based NeRF exceed the single GPU memory limit and scale
to larger scenes. However, this approach requires multiple background NeRF to
handle out-of-partition rays, which leads to redundancy of learning. Inspired
by the fact that the background of current partition is the foreground of
adjacent partition, we propose a scalable scene reconstruction method based on
joint Multi-resolution Hash Grids, named DistGrid. In this method, the scene is
divided into multiple closely-paved yet non-overlapped Axis-Aligned Bounding
Boxes, and a novel segmented volume rendering method is proposed to handle
cross-boundary rays, thereby eliminating the need for background NeRFs. The
experiments demonstrate that our method outperforms existing methods on all
evaluated large-scale scenes, and provides visually plausible scene
reconstruction. The scalability of our method on reconstruction quality is
further evaluated qualitatively and quantitatively.

基于多分辨率哈希格方法的可扩展场景重建方法，通过划分大型场景、消除背景 NeRF，并利用分割体积渲染方法处理跨边界射线，实现了更高质量、更可扩展的场景重建。

DistGrid：具有分布式多分辨率哈希网格的可扩展场景重建

DistGrid: Scalable Scene Reconstruction with Distributed  Multi-resolution Hash Grid

Training of modern large neural networks (NN) requires a combination of
parallelization strategies encompassing data, model, or optimizer sharding.
When strategies increase in complexity, it becomes necessary for partitioning
tools to be 1) expressive, allowing the composition of simpler strategies, and
2) predictable to estimate performance analytically. We present PartIR, our
design for a NN partitioning system. PartIR is focused on an incremental
approach to rewriting and is hardware-and-runtime agnostic. We present a simple
but powerful API for composing sharding strategies and a simulator to validate
them. The process is driven by high-level programmer-issued partitioning
tactics, which can be both manual and automatic. Importantly, the tactics are
specified separately from the model code, making them easy to change. We
evaluate PartIR on several different models to demonstrate its predictability,
expressibility, and ability to reach peak performance..

现代大型神经网络（NN）的训练需要结合数据、模型或优化器分片等并行化策略。当策略变得越来越复杂时，分区工具需要具备 1）表达能力，以允许复合简单的策略，2）可预测性，以便分析性能估计。我们提出了 PartIR，一种 NN 分区系统的设计。PartIR 着重于增量重写，并且不依赖于具体硬件和运行时环境。我们提供了一个简单但强大的 API 用于组合分片策略，并提供模拟器进行验证。该过程由高级程序员指定的分区策略驱动，可以手动或自动执行。重要的是，这些策略与模型代码分开指定，易于更改。我们在多个不同的模型上评估了 PartIR，以展示其可预测性、表达能力和达到峰值性能的能力。

PartIR: 机器学习 SPMD 划分策略的构建

PartIR: Composing SPMD Partitioning Strategies for Machine Learning

Nowadays, many AI applications utilizing resource-constrained edge devices
(e.g., small mobile robots, tiny IoT devices, etc.) require Convolutional
Neural Network (CNN) inference on a distributed system at the edge due to
limited resources of a single edge device to accommodate and execute a large
CNN. There are four main partitioning strategies that can be utilized to
partition a large CNN model and perform distributed CNN inference on multiple
devices at the edge. However, to the best of our knowledge, no research has
been conducted to investigate how these four partitioning strategies affect the
energy consumption per edge device. Such an investigation is important because
it will reveal the potential of these partitioning strategies to be used
effectively for reduction of the per-device energy consumption when a large CNN
model is deployed for distributed inference at the edge. Therefore, in this
paper, we investigate and compare the per-device energy consumption of CNN
model inference at the edge on a distributed system when the four partitioning
strategies are utilized. The goal of our investigation and comparison is to
find out which partitioning strategies (and under what conditions) have the
highest potential to decrease the energy consumption per edge device when CNN
inference is performed at the edge on a distributed system.

本文研究和比较了在边缘部署大型 CNN 模型进行分布式推断时，利用四种划分策略对 CNN 模型推断造成的每个设备的能量消耗量，并旨在找出哪种划分策略（以及在什么条件下）可以在分布式系统中降低边缘设备的能耗。