Traditionally, original equipment manufacturers (OEMs) send device-specific over-the-air (OTA) packages to ensure the latest firmware, security patches, etc. With millions of IoT devices, even a tiny per-centage of OTA failures will result in tens of thousands of globally affected consumers. The state-of-the-art OTA methods are suited for high-end Android & embedded Linux devices and not for resource-constrained devices (e.g. MCUs, small CPUs) with only a few MB memory. Current OTA methods have been tested only on non-ML use-cases such as remote bugs patching or security flaws, etc. In this paper, we present OTA-TinyML approach that, via HTTPS, loads the C source file of ML models from a webserver into IoT boards. OTA-TinyML does not strain hardware (resource-friendly) as its implementation spans only a few lines of code. It is compatible with a range of ML models (e.g. text, speech, image domains) and MCUs (e.g. Cortex M series, STM32, Xtensa). OTA-TinyML is tested by performing remote fetching of 6 types of ML models, storing them on 4 types of memory units, then loading and executing on 7 popular Arduino IoT boards.

TinyML: Tiny in size, big in impact! This paper presents TinyML-CAM pipeline for real-time and memory-efficient image recognition on IoT boards. TinyML-CAM can be used by developers to implement their customized tasks in ≈ 30 minutes, with minimal code configuration. We evaluated TinyML-CAM by using it to create a RandomForestClassifier (RF) based real-time image recognition system, which ran at 80 FPS and consumed only 1 kB of RAM on ESP32.