TinyD\'ej\`aVu: Smaller RAM and Faster Inference with Neural Networks on MCUs for Sensor Data Streams

ArXi:2512.09786v2 Announce Type: replace Examples of embedded intelligence include a wide variety of tiny neural networks used on-board wireless sensors and actuators, which are expected to continuously perform inference on time-series of the data they sense. In order to fit lifetime and energy consumption requirements when operating on battery, such hardware is exclusively based on microcontroller with as little memory as possible, e.g., 128 kB of RAM. In this context, optimizing data flows during inference across neural network layers becomes crucial. In this paper, we.