Tiny Neural Networks for Tiny IoT Devices

Over the past decade, pervasive computing has been influenced by two major trends. First, the widespread adoption of smart, connected, and always active Internet of Things (IoT) devices, pushed by advancements in modern microcontrollers, which now offer more powerful processors, greater efficiency, and faster clock speeds Second, deep learning techniques have grown exponentially, driven by faster processors and the availability of large training datasets. However, traditional deep learning models are often too resource-intensive for embedded systems and can only be run on powerful (and power-hungry) general-purpose GPUs (GP-GPUs) [1] , [2] . The emerging field of TinyML is addressing the gap between small, low-power embedded devices and the high computational requirements of modern deep-learning workloads. This cutting-edge field is working on applying the transformative power of machine learning (ML) in the constrained environment of tiny devices and embedded systems [3] . TinyML has been successfully applied across various domains, including audio processing and sound event detection [1] , biosignal analysis [4] , gesture recognition [5] , and general time-series data. Among these areas, computer vision stands out as one of the most prominent applications of TinyML - allowing advanced multimedia processing algorithms to run on tiny, power-efficient devices. We propose two novel architectures to optimize the current resources and performance tradeoffs in embedded devices.