tinyml

**TinyML** is the **field of deploying machine learning models on ultra-low-power microcontrollers (MCUs) with kilobytes of memory** — enabling AI inference on devices that cost under $1, run on coin-cell batteries for years, and are embedded in sensors, wearables, and industrial equipment. **TinyML Constraints** - **Memory**: 256KB-1MB flash, 64-256KB RAM — models must be extremely small. - **Compute**: ARM Cortex-M class processors — no GPU, limited integer/fixed-point arithmetic. - **Power**: Microwatt to milliwatt power budgets — must run on batteries for years. - **Frameworks**: TensorFlow Lite Micro, microTVM, CMSIS-NN for optimized inference. **Why It Matters** - **Ubiquitous AI**: TinyML enables AI everywhere — in every sensor, actuator, and embedded device. - **Semiconductor Sensors**: Embed ML directly in process sensors for real-time, on-device anomaly detection. - **Always-On**: Ultra-low power enables always-on sensing and inference without cloud connectivity. **TinyML** is **AI on the smallest computers** — deploying machine learning on microcontrollers for ubiquitous, always-on, battery-powered intelligence.