Modern computationally-intensive applications often operate under time
constraints, necessitating acceleration methods and distribution of
computational workloads across multiple entities. However, the outcome is
either achieved within the desired timeline or not, and in the latter case,
valuable resources are wasted. In this paper, we introduce solutions for
layered-resolution computation. These solutions allow lower-resolution results
to be obtained at an earlier stage than the final result. This innovation
notably enhances the deadline-based systems, as if a computational job is
terminated due to time constraints, an approximate version of the final result
can still be generated. Moreover, in certain operational regimes, a
high-resolution result might be unnecessary, because the low-resolution result
may already deviate significantly from the decision threshold, for example in
AI-based decision-making systems. Therefore, operators can decide whether
higher resolution is needed or not based on intermediate results, enabling
computations with adaptive resolution. We present our framework for two
critical and computationally demanding jobs: distributed matrix multiplication
(linear) and model inference in machine learning (nonlinear). Our theoretical
and empirical results demonstrate that the execution delay for the first
resolution is significantly shorter than that for the final resolution, while
maintaining overall complexity comparable to the conventional one-shot
approach. Our experiments further illustrate how the layering feature increases
the likelihood of meeting deadlines and enables adaptability and transparency
in massive, large-scale computations.

我们介绍了一种分层分辨率计算的解决方案，使得在时间约束下可以在较早的阶段获得低分辨率的结果，提高了基于截止日期的系统的效果；并且我们的实验结果显示，分层特性增加了满足截止日期的可能性，使得大规模计算具备了适应性和透明度。

大规模计算中的连续改进：推进模型推理应用

Successive Refinement in Large-Scale Computation: Advancing Model  Inference Applications

Vision transformers (ViTs) have emerged as a prevalent architecture for
vision tasks owing to their impressive performance. However, when it comes to
handling long token sequences, especially in dense prediction tasks that
require high-resolution input, the complexity of ViTs increases significantly.
Notably, dense prediction tasks, such as semantic segmentation or object
detection, emphasize more on the contours or shapes of objects, while the
texture inside objects is less informative. Motivated by this observation, we
propose to apply adaptive resolution for different regions in the image
according to their importance. Specifically, at the intermediate layer of the
ViT, we utilize a spatial-aware density-based clustering algorithm to select
representative tokens from the token sequence. Once the representative tokens
are determined, we proceed to merge other tokens into their closest
representative token. Consequently, semantic similar tokens are merged together
to form low-resolution regions, while semantic irrelevant tokens are preserved
independently as high-resolution regions. This strategy effectively reduces the
number of tokens, allowing subsequent layers to handle a reduced token sequence
and achieve acceleration. We evaluate our proposed method on three different
datasets and observe promising performance. For example, the "Segmenter ViT-L"
model can be accelerated by 48% FPS without fine-tuning, while maintaining the
performance. Additionally, our method can be applied to accelerate fine-tuning
as well. Experimental results demonstrate that we can save 52% training time
while accelerating 2.46 times FPS with only a 0.09% performance drop. The code
is available at this https URL

通过自适应分辨率和聚类算法，提出一种用于处理密集预测任务中长序列的视觉 Transformer 方法，实现对低分辨率和高分辨率区域的区分，从而加速模型并获得有希望的表现。

AiluRus：一个用于密集预测的可扩展 ViT 框架

AiluRus: A Scalable ViT Framework for Dense Prediction

Most monocular depth sensing methods use conventionally captured images that
are created without considering scene content. In contrast, animal eyes have
fast mechanical motions, called saccades, that control how the scene is imaged
by the fovea, where resolution is highest. In this paper, we present the
SaccadeCam framework for adaptively distributing resolution onto regions of
interest in the scene. Our algorithm for adaptive resolution is a
self-supervised network and we demonstrate results for end-to-end learning for
monocular depth estimation. We also show preliminary results with a real
SaccadeCam hardware prototype.

本文提出了一种名为 SaccadeCam 的方法，通过自适应地分配分辨率到场景中的兴趣区域，实现单目深度估计的端到端学习。该算法将充分考虑物体在视觉系统中的感知特性。