Percepta让边缘设备高效处理实时数据流，支持强化学习持续决策。
Percepta: High Performance Stream Processing at the Edge
- 轻量级数据流处理系统，专为边缘AI设计
- 支持奖励函数计算与模型重训练，实现持续学习
- 适配多源异构数据，自动处理缺失与速率不一
实时数据激增和物联网（IoT）设备的普及暴露了以云为中心方案在延迟、带宽和隐私方面的局限性，推动了边缘计算的发展。与物联网相关的挑战包括：多源数据速率协调、协议转换、数据丢失处理以及与人工智能（AI）模型的集成。本文提出Percepta，一种面向边缘AI工作负载的轻量级数据流处理（DSP）系统，特别针对强化学习（RL）场景。系统具备奖励函数计算、模型重训练所需数据存储、实时数据准备等特性，支持持续决策。此外，还提供数据归一化、跨异构协议与采样率的数据统一，以及对缺失或不完整数据的鲁棒处理，有效应对边缘AI部署中的实际挑战。
原文摘要 · Abstract (English)
The rise of real-time data and the proliferation of Internet of Things (IoT) devices have highlighted the limitations of cloud-centric solutions, particularly regarding latency, bandwidth, and privacy. These challenges have driven the growth of Edge Computing. Associated with IoT appears a set of other problems, like: data rate harmonization between multiple sources, protocol conversion, handling the loss of data and the integration with Artificial Intelligence (AI) models. This paper presents Percepta, a lightweight Data Stream Processing (DSP) system tailored to support AI workloads at the edge, with a particular focus on such as Reinforcement Learning (RL). It introduces specialized features such as reward function computation, data storage for model retraining, and real-time data preparation to support continuous decision-making. Additional functionalities include data normalization, harmonization across heterogeneous protocols and sampling rates, and robust handling of missing or incomplete data, making it well suited for the challenges of edge-based AI deployment.
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