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Ultra-low-power machine learning models for ARM Cortex-M and ESP32 silicon, vibration FFT analysis, and battery-powered sensory processing.

1 published publications•All Articles

IoT & Embedded Engineering

TinyML & Edge AI: Quantized Neural Networks on Sub-50mW Microcontrollers

How 8-bit integer quantization (INT8) and CMSIS-NN enable sub-15ms vibration and acoustic anomaly detection on ARM Cortex-M microcontrollers while slashing cellular data costs by 92%.

#TinyML #Edge AI #IoT #Microcontrollers

9/22/2026

8 min

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KNetwork Systems Engineering

Building complex systems with #TinyML?

Our engineering team designs sub-50ms distributed backends, edge AI pipelines, and high-concurrency systems.

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