# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
###### Abstract
In order to establish goals and address the difficulties of this novel and experimental, interdisciplinary topic, TinyML development requires a collaborative effort between embedded systems and the machine learning communities. We evaluate the present state of TinyML in this survey, which aims to provide artificial intelligence to smaller, less powerful edge devices, examine the TensorFlow Lite framework, which is among the most important frameworks for the growth of TinyML systems, and discuss a few enabling factors for future expansion of TinyML. The absence of a suitable framework has delayed the acceptance and deployment of TinyML in applications. In addition to building a model on an embedded target, the framework should enable model training on a high computing platform. Model orchestration and testing, which are helpful for manufacturing devices, should be used by TinyML together with a broad ecosystem of machine learning technologies. In this paper, TFLM will be examined at in order to introduce deep learning to embedded devices and so significantly widen the applicability of ML. The TFLM is a framework designed particularly for deep learning on microcontrollers with extremely little storage (a few kilobytes). The key contributions of TFLM are its design decisions to solve the particular issues of embedded systems: Hardware diversity in a dispersed environment, missing software functionalities, and extreme resource restrictions. In conclusion, this study presents the historical evolution of TinyML, a thorough description of this technology, and a methodical assessment of prior research efforts to offer specific recommendations for future research. In order to fill this gap, we highlight benefits of TinyML, applications, and use cases as well as the substantial contributions made by industry and academics. These contributions help scholars gain a deeper comprehension of the fundamental ideas of TinyML. The key contributions of this survey are as follows:  
* Presentation of major performance measures for the TinyML framework, as well as its definition and overview; examination of important technologies. * Review of research on TinyML conducted by various research groups and creation of an academic map. * Identify important obstacles and give a future direction for TinyML research in which we cover numerous concerns. * To assemble a list of existing TinyML-based toolkits for training and model construction at the edge, where hardware platforms, software programs, and libraries are available. * Present and analyze TinyML application areas, as well as illustrate various TinyML use cases. The remainder of the article is arranged as follows. A comprehensive overview of TinyML, with all of its features, is provided in Section 2. In Section 3, a detailed analysis of the TensorFlow Lite-TFLM framework for use in TinyML applications is provided. Section 4 illustrates the integration of TinyML with network technologies. Section 5 discusses the technology of TinyML and conclusions about it. Finally, Section 6 presents the future directions for TinyML that will have an important role in its development. In continuation of the above, the goal of this journal is to provide a thorough overview of this ground-breaking technology to everyone with an interest in the topic, in addition to the in-depth research section. In conclusion, this systematic review will serve as an informative cornerstone for the research community, pave the way for further research in this direction, and serve as a roadmap for understanding the new, emerging field of TinyML.