In Edge AI applications like predictive maintenance or gesture recognition, data streams continuously from an IMU (Accelerometer/Gyroscope) or microphone. Microcontrollers cannot feed this continuous stream directly into a Neural Network; the data must be chunked into a Sliding Window.

FIFO Buffers & Window Overlap

To ensure the AI doesn't miss a mechanical anomaly or gesture that happens exactly between two windows, we use an Overlap (typically 50%). This means your MCU must manage a continuous circular buffer (FIFO) via DMA, and copy the relevant chunks into a static Tensor Arena for inference.

$$Window\_Size\_(Bytes) = \frac{f_s \times T_{window}}{1000} \times Axes \times Resolution$$

Miscalculating these buffers is the #1 cause of Hard Faults and RAM overflows in streaming TinyML applications. Use the tool below to calculate your exact RAM requirements and the required Inference Rate (how fast your MCU must run the AI to keep up with the sensor).

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