# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
###### Abstract  
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices. In particular, the TinyML framework in such devices aims to deliver reduced latency, efficient bandwidth consumption, improved data security, increased privacy, lower costs and overall network cost reduction in cloud environments. Its ability to enable IoT devices to work effectively without constant connectivity to cloud services, while nevertheless providing accurate ML services, offers a viable alternative for IoT applications seeking cost-effective solutions. TinyML intends to deliver on-premises analytics that bring significant value to IoT services, particularly in environments with limited connection. This review article defines TinyML, presents an overview of its benefits and uses and provides background information based on up-to-date literature. Then, we demonstrate the TensorFlow Lite framework which supports TinyML along with analytical steps for an ML model creation. In addition, we explore the integration of TinyML with network technologies such as 5G and LPWAN. Ultimately, we anticipate that this analysis will serve as an informational pillar for the IoT/Cloud research community and pave the way for future studies. Internet of Things; large scale IoT deployments; TinyML; edge computing; edge AI; edge ML; machine learning; deep learning; Tensorflow Lite; TFLM; LPWAN; 5G +
Footnote †: journal: 6 December 2022  
Along with the advent of the artificial intelligence and machine learning sectors, advancements in connectivity and the proliferation of system-on-chip solutions have fueled the expansion of the Internet of Things (IoT) industry. Nowadays, the number of products incorporating microcontrollers is expanding at an exponential rate. Over 250 billion microcontrollers are in use, with 40 billion microcontrollers scheduled to be deployed by the end of 2022 [7]. This trend is further shown in Figure 2.  
In several circumstances, however, embedded systems do not handle the data they gather; rather, the data are transported to a distant site for storage and subsequent processing. In some applications, this might result in undesirable delay, data leaks, and privacy concerns. This has sparked research into the fundamental difficulties associated with developing machine learning algorithms on embedded devices with restricted resources (in terms of memory, processor speed and power) [8]. Information is frequently gathered by embedded devices from a number of sensors (audio, IR cameras, IMU, accelerometers, temperature sensors, CMOS cameras, chemical sensors, etc.). These embedded systems often are powered by batteries and consume little energy (1 mW or less). Because that primarily performs simple processing tasks, the CPU of the device is often left idle over time. They are ideal for developing lightweight inference algorithms due to the ubiquitous usage of MCU-powered embedded devices, their latent processing capabilities, and their closeness to physical data sources via sensors. Tiny machine learning (TinyML), a new embedded technology industry aiming at building machine learning algorithms on resource-constrained devices based on MCUs, arose from this technique [9]. TinyML will allow advancements in a variety of fields, including consumer electronics, autonomous systems, healthcare, distributed cyber-physical systems, smart agriculture,  
Figure 1: The advancement of computing technology over time. Figure 2: The history of the MCU market [7]. and the larger subject of artificial intelligence. Furthermore, accomplishing these objectives necessitates collaborative solutions from a variety of disciplines, including machine learning, optimization, hardware design, computer architecture and signal processing, among several others.