The network edge's role in Artificial Intelligence (AI) inference processing
is rapidly expanding, driven by a plethora of applications seeking
computational advantages. These applications strive for data-driven efficiency,
leveraging robust AI capabilities and prioritizing real-time responsiveness.
However, as demand grows, so does system complexity. The proliferation of AI
inference accelerators showcases innovation but also underscores challenges,
particularly the varied software and hardware configurations of these devices.
This diversity, while advantageous for certain tasks, introduces hurdles in
device integration and coordination. In this paper, our objectives are
three-fold. Firstly, we outline the requirements and components of a framework
that accommodates hardware diversity. Next, we assess the impact of device
heterogeneity on AI inference performance, identifying strategies to optimize
outcomes without compromising service quality. Lastly, we shed light on the
prevailing challenges and opportunities in this domain, offering insights for
both the research community and industry stakeholders.

网络边缘在人工智能（AI）推理处理中的作用正在快速扩大，驱动力是众多寻求计算优势的应用程序。

异构受限计算中的边缘 AI 推断：可行性与机遇

Edge AI Inference in Heterogeneous Constrained Computing: Feasibility  and Opportunities

Instance segmentation is applied widely in image editing, image analysis and
autonomous driving, etc. However, insufficient data is a common problem in
practical applications. The Visual Inductive Priors(VIPriors) Instance
Segmentation Challenge has focused on this problem. VIPriors for Data-Efficient
Computer Vision Challenges ask competitors to train models from scratch in a
data-deficient setting, but there are some visual inductive priors that can be
used. In order to address the VIPriors instance segmentation problem, we
designed a Task-Specific Data Augmentation(TS-DA) strategy and Inference
Processing(TS-IP) strategy. The main purpose of task-specific data augmentation
strategy is to tackle the data-deficient problem. And in order to make the most
of visual inductive priors, we designed a task-specific inference processing
strategy. We demonstrate the applicability of proposed method on VIPriors
Instance Segmentation Challenge. The segmentation model applied is Hybrid Task
Cascade based detector on the Swin-Base based CBNetV2 backbone. Experimental
results demonstrate that proposed method can achieve a competitive result on
the test set of 2022 VIPriors Instance Segmentation Challenge, with 0.531
AP@0.50:0.95.

本文旨在解决实际应用中数据不足的问题，通过提出 Task-Specific Data Augmentation (TS-DA) 策略和 Inference Processing (TS-IP) 策略的方法，充分利用视觉归纳因素，提高 VIPriors Instance Segmentation Challenge 上的表现。经过实验证明，提出的方法在 2022 年 VIPriors Instance Segmentation Challenge 的测试集中达到竞争水平，并取得了 0.531 的 AP@0.50:0.95。