# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML  
### Overview  
Because it has the potential to improve privacy, autonomy, responsiveness, and energy efficiency of edge devices, machine learning (ML)-based inference is becoming more and more alluring [10; 11; 12]. Up until now, machine learning models like quantum, using pruning, and sparsity have significantly improved as a result of the majority of edge ML research focusing on inference on mobile devices. Meanwhile, substantial strides have been made in recent years to broaden the reach of edge systems. Expanding the use of edge ML to devices in the microcontroller category is of interest to both academia [10; 11] and businesses [13; 14]. The field of machine learning known as tiny machine learning (TinyML) is expanding quickly. It consists of hardware, software, and algorithms that can analyze sensor data using ultra-low power devices, opening up a wide range of continuously available use cases for battery-powered devices. TinyML systems are gradually being utilized for a variety of commercial applications, with additional systems on the horizon, as substantial breakthroughs in algorithms, networks, and models are produced. In addition, what were originally considered low-power applications are currently commonplace and widely available. As a result, there is increasing impetus, as shown by technological advancements, ecosystem expansion, and the requirement for benchmarking and assessment approaches. We emphasize both challenges and opportunities and we provide an overview of the current state of the art in this field. By providing ML inference to extremely low power, often a milliwatt or less, to devices with constrained storage, TinyML [15] aims to break the conventional power barrier that restricts globally distributed machine intelligence. TinyML increases privacy protection and responsiveness while lowering energy costs associated with wireless transmission, which at this scale are substantially more than those of computation, by doing inference near to the sensor and on the device [16]. A new class of intelligent, always-on, battery-powered apps that have the potential to revolutionize real-time data collection and processing is also made possible by efficiency of the TinyML. This rapidly expanding sector, which is the result of various innovations, is only expected to develop more quickly in the upcoming years [17]. IoT solutions that can lessen the burden of frequent data access and transfer to the cloud are necessary as the volume of data produced by IoT devices increases. Integration of TinyML into IoT devices is one way to address these drawbacks, according to source [18] TinyML is a framework that does not require constant connection to overloaded cloud services and runs on MCUs inside IoT devices. IoT ML services can therefore be provided to IoT devices or processed inside the IoT device in the current context [19]. The first approach is conventional, relying on the edge and cloud for the IoT-based system to provide ML services [19]. The second approach, in contrast, integrates TinyML into a contemporary framework to give IoT devices intelligence, which suggests that the MCU that interacts with the sensors may run the ML algorithm locally to forecast sensor data. Although the integration of ML into MCUs is mostly unexplored, first findings from the incorporation of the TinyML framework into IoT devices suggest that the area offers significant promise for ML execution at the very edge of the IoT. The next phase of hyper-digitization is thought to be TinyML. To fit into the constraining limitations of MCU-class devices, TinyML models in particular must be compact enough (for example, constrained embedded computing capability of the order of MHz of processor clock frequency and a few hundred kB of memory), which restricts the input size and number of layers [11] or calls for the use of lightweight methods that do not rely on neural networks [20].