TinyD\'ej\`aVu: Smaller RAM and Faster Inference with Neural Networks on MCUs for Sensor Data Streams
arXiv:2512.09786v2 Announce Type: replace Abstract: 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 introduce a
The proliferation of IoT devices and the increasing demand for on-device intelligence necessitate innovations in efficient AI deployment on constrained hardware.
This development allows for more pervasive and energy-efficient AI at the edge, unlocking new applications in embedded systems and significantly reducing data transfer and energy consumption.
The ability to run more complex neural networks on microcontrollers with extremely limited RAM changes the paradigm for embedded AI, making sophisticated local intelligence more feasible.
- · IoT device manufacturers
- · Edge AI software developers
- · Proprietary sensor manufacturers
- · Energy-efficient computing sector
- · Legacy cloud-dependent IoT platforms
- · Companies reliant on large data center inference for all AI tasks
Widespread adoption of AI in battery-powered, data-sensitive applications due to enhanced local processing capabilities.
Reduced reliance on constant cloud connectivity for many monitoring and inference tasks, leading to improved privacy and robustness in distributed systems.
Accelerated development of autonomous edge devices operating in environments with intermittent or no network access, impacting defense, agriculture, and remote sensing.
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