# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### Overview
A few examples of TinyML tools are effective inference tools [21; 22], frameworks (TensorFlow), memory-conscious neural architecture searches [10], and aggressive quantum methods [23]. The next generation of general-purpose MCUs will have more documentation, hardware intended specifically for low-power inference will be built, and new architectures will be created just for task-specific inference engines, according to research on TinyML hardware [13; 24]. The intricacy and dynamics of the field conceal progress measurement and make dynamism planning decisions harder. To enable continuous innovation, a fair and trustworthy comparison method is essential. Increased hardware capabilities are typically the result of innovation, hence a reliable TinyML hardware evaluation is necessary. For many common ML use cases, TinyML may seem futuristic. As ultra-low-powered inference hardware advances, the feasibility barrier rises [25]. Although object counting and image classification in a large label space are suitable for low-power continuous-operation applications, TinyML technology is still too computationally and memory intensive to handle these tasks. The global market for edge computing is estimated to reach $1.12 trillion by 2023, according to [26]. IoT layer improvements must be aggressively pushed in order to lessen this burden, with a focus on ML in IoT devices. With companies like Ericsson already offering TinyML-as-a-Service solutions, the expanding need for smart devices has also been magnificently matched by the global research community [19]. With less reliance on cloud services, TinyML is anticipated to play a significant role in the provision of intelligent IoT solutions in the near future. It will additionally change the way IoT services are used in places with limited internet connection. Additionally, TinyML will be crucial to next technologies. For instance, many of the crucial parts of augmented reality (AR) glasses are constantly active and powered by batteries. Such devices are unable to wait for calculations to upload to a cloud, edge server, or even another mobile device because of real-time constraints. As a result of frequent restrictions, developments in TinyML [27] potentially assist AR applications tremendously. As businesses look for AI solutions in areas like environmental monitoring, people tracking, image or video control and voice activation, consumers have encountered difficulties due to the drawbacks of embedded modules and battery-powered sensors that perform with modest computing resources provided by general-purpose microcontrollers. Huge amounts of data usually need the use of sensors and edge devices. Additionally, due to their low power consumption, these devices struggle to maintain high data throughput and computational performance which causes latency problems. \"Given that AI is being used to make quick choices in areas like quality control, alarm management and surveillance, any lag in the system might result in machine downtime or slowness, which would cause significant damage or loss of production. By moving AI to the edge, possible risks and vulnerabilities including erratic connectivity and sluggish answers are reduced\", a principal analyst at ABI Research, [PERSON], stated as an example [28]. The most essential, of however, is not just hardware development, which speeds up TinyML democratization [29]. TensorFlow Lite, the development of open source microcontroller software by Google, and for-profit solutions from companies such as SensiML provide libraries and developer-friendly software tools, enabling more engineers to build AI models that can manage really edge applications. It is no longer sufficient to create capable and distinctive hardware. TinyML hardware producers must focus on establishing new AI development environments or joining those that already exist, embracing open source, and highlighting to customers their distinct selling points and niche markets.