Brain-computer interface (BCI) has garnered the significant attention for
their potential in various applications, with event-related potential (ERP)
performing a considerable role in BCI systems. This paper introduces a novel
Distributed Inference System tailored for detecting task-wise single-trial ERPs
in a stream of satellite images. Unlike traditional methodologies that employ a
single model for target detection, our system utilizes multiple models, each
optimized for specific tasks, ensuring enhanced performance across varying
image transition times and target onset times. Our experiments, conducted on
four participants, employed two paradigms: the Normal paradigm and an AI
paradigm with bounding boxes. Results indicate that our proposed system
outperforms the conventional methods in both paradigms, achieving the highest
$F_{\beta}$ scores. Furthermore, including bounding boxes in the AI paradigm
significantly improved target recognition. This study underscores the potential
of our Distributed Inference System in advancing the field of ERP detection in
satellite image streams.

该研究介绍了一种适用于在卫星图像流中检测任务式单次事件相关电位的分布式推理系统，通过利用多个优化于特定任务的模型，确保在各种图像转换时间和目标出现时间下实现卓越性能，在两个范式中试验结果表明，该系统在目标检测中优于传统方法，并且在 AI 范式中引入边界框显著提高了目标识别效果。

一种用于在卫星图像流中检测任务单次试验事件相关电位的分布式推理系统

A Distributed Inference System for Detecting Task-wise Single Trial  Event-Related Potential in Stream of Satellite Images

Despite the soaring use of convolutional neural networks (CNNs) in mobile
applications, uniformly sustaining high-performance inference on mobile has
been elusive due to the excessive computational demands of modern CNNs and the
increasing diversity of deployed devices. A popular alternative comprises
offloading CNN processing to powerful cloud-based servers. Nevertheless, by
relying on the cloud to produce outputs, emerging mission-critical and
high-mobility applications, such as drone obstacle avoidance or interactive
applications, can suffer from the dynamic connectivity conditions and the
uncertain availability of the cloud. In this paper, we propose SPINN, a
distributed inference system that employs synergistic device-cloud computation
together with a progressive inference method to deliver fast and robust CNN
inference across diverse settings. The proposed system introduces a novel
scheduler that co-optimises the early-exit policy and the CNN splitting at run
time, in order to adapt to dynamic conditions and meet user-defined
service-level requirements. Quantitative evaluation illustrates that SPINN
outperforms its state-of-the-art collaborative inference counterparts by up to
2x in achieved throughput under varying network conditions, reduces the server
cost by up to 6.8x and improves accuracy by 20.7% under latency constraints,
while providing robust operation under uncertain connectivity conditions and
significant energy savings compared to cloud-centric execution.

本文提出了 SPINN 系统，利用分布式计算和渐进推理方法共同实现设备云协同计算，并引入了一种新的调度程序，通过合理设置早期退出策略和 CNN 分割，以适应动态条件和满足用户定义的服务水平要求，以达到快速和强大的 CNN 推理。定量评估表明，在不同网络条件下，SPINN 的吞吐量高达其最先进的协作推理对手的 2 倍，将服务器成本降低了多达 6.8 倍，并在延迟限制下提高精度 20.7％，同时在不确定的连接条件下提供稳健操作和与云为中心的执行相比显著的节能。