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Towards Stream Learning on Embedded Systems: Benchmarking the Memory Consumption of Stream Learning Methods

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Stream learning is commonly evaluated through predictive performance and adaptation to concept drift. However, sustained operation of a stream learner also requires predictable and bounded resource usage even on long streams. This requirement becomes even more critical when learning moves from servers to near-sensor embedded systems where memory and processing are scarce resources. In state-of-the-art stream learning, however, we perceive a strong focus on concept drift adaptation, whereas resource usage is often an evaluation byproduct. To close this gap, we benchmark seven representative str

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