Percepta: High Performance Stream Processing at the Edge
This addresses the problem of deploying AI in edge computing environments for IoT applications, though it appears incremental as it builds on existing stream processing concepts.
The paper tackles the challenges of real-time data processing for AI at the edge, such as latency and data heterogeneity, by presenting Percepta, a lightweight stream processing system that supports AI workloads like reinforcement learning with features for reward computation and data preparation.
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.