A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.
Benchmarking TinyML Systems: Challenges and Direction
2 Pith papers cite this work. Polarity classification is still indexing.
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Network-adaptive encoding reduces end-to-end latency in cloud-based visual preprocessing for neuroprostheses during congestion while preserving global scene structure at the cost of sharper boundary degradation.
citing papers explorer
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Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data
A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.
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Network-Adaptive Cloud Processing for Visual Neuroprostheses
Network-adaptive encoding reduces end-to-end latency in cloud-based visual preprocessing for neuroprostheses during congestion while preserving global scene structure at the cost of sharper boundary degradation.