# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 5 Discussion and Future Directions
The primary contributions of TFLM are the design choices that must be taken to address the unique challenges of embedded systems, such as the lack of software features, the severe resource limits, and the heterogeneity of hardware in a fragmented environment. TinyML, in particular, has created a new class of machine learning frameworks aimed towards edge devices in the IoT ecosystem that run on batteries and have limited capabilities. Although academics have concentrated their efforts on developing software frameworks that try to provide a shortened version of the ML model for inclusion into MCUs, improving the model even so presents a demanding task in the TinyML society. In a variety of applications, TinyML use cases are occurring more often. As a result, the efficacy of the framework should be ensured in order to stimulate innovation and additional investment in this sector. As a result, TinyML requires an effective benchmarking mechanism. Based on our study thus far, we anticipate the creation of a TinyML benchmarking solution that is exact, simple, quick, and reliable enough to be applicable to a variety of IoT applications. Finally, TinyML is encouraging the community to overcome issues that have never been addressed at such a level previously. Developers of TinyML must understand how data is used, how it is displayed, and what data is used. We could be asked to create unconnected, pre-installed systems with out-of-date software. A TinyML device may never have a network connection, which raises the bar even higher and means it cannot be the subject of an attack from an adversary or breach the privacy of a user. However, newer, more accurate alternatives can never take the place of such equipment. There are simply too many potential uses for compact sensors with integrated machine learning. The majority of these possibilities are in places without electricity, and even when power is available, additional constraints typically dictate reduced processing capability, near-zero cooling, limited communication capabilities and a tiny form factor. Last but not least, one of the most underestimated issues with TinyML is the incorporation of so-called \"ethical AI\" [37]. We must make sure that these products always adhere to what we, as humans, find ethical. As was previously said, rapid rise of TinyML has permitted products and systems that are now firmly ingrained in our culture. While aiming to use less resources, we must make sure that these systems were trained and designed without bias. Moreover, we analyze a future roadmap related to TinyML and based on 5 key components [30]:  
* New dimension: A variety of variables are claimed to influence the development and acceptance of the TinyML standard in the future. An intelligent edge system, for example, necessitates edge software that integrates sophistication, edge-device intelligence consistency, real-time learning, distributed learning, online learning, and data-network management. This partnership should be expanded to strengthen local security and privacy by allowing end-user context to be stored on edge devices. Additionally, it should give priority to improving edge device infrastructure, low-cost knowledge sharing capabilities in edge device systems and edge device platform orchestration. Figure 18 emphasizes the contributions of these factors to the evolution of the TinyML paradigm. * Support for portability: Another issue that may be overcome by establishing improved compute data delivery methods on Edge platforms is portability support. Location-aware optimization can conserve network capacity and boost network spectrum coverage for edge devices, allowing them to collaborate with neighboring devices. It can therefore contribute to the progressive transfer of know-how and models for others to utilize. Furthermore, the quality of service may be altered to forecast how edge devices would behave when interacting with neighboring nodes.