# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 5 Discussion and Future Directions
These data may be exchanged across nodes or clouds in the immediate vicinity to forecast the intelligent orientation of edge devices. * Edge Intelligence Framework: The following should be the foundation of a standard edge intelligence framework: (i) energy-efficient management, (ii) dynamic task distribution, (iii) data intelligence, (iv) wireless networking, (v) collaborative intelligence, (vi) predictive service quality, (vii) communication service implementation, (viii) real-time inference, (ix) liquid software propagation between edge nodes, and (x) machine learning as a service. From a future-proof learning perspective, take into account implementing such TinyML arrangements to make them compatible with 5G and 6G technologies. Therefore, mmWave shaul systems have to be included to TinyML systems to enhance ML models optimization. Hypercooperation between the cloud and the edge may thus be implemented in an effective manner [168; 169; 170]. * Offloading operations: The activation/deactivation of processing tasks can actually be exploited during machine learning scenarios triggered by the edge. These loading methods ought to be included to the dynamic configuration of edge-aware features that is already accessible. Thus, TinyML will enhance the transmission of resource-intensive processes from edge devices with low resources. Interoperability between the edge and the cloud could become more focused as a result. It is necessary to undertake investigations to determine the underlying processes that support these resource allocations (e.g., machine learning, channel bandwidth, memory chunks, data recruitment, sensing capabilities, CPU cycles). * Future Perspective: TinyML is progressively becoming a must and a reality for making educated decisions in everyday life. Especially for low-power embedded devices used in a variety of applications. To provide consumers with an upgraded user experience, mobile platforms must adjust their orientation to TinyML. TinyML-aware approaches for next age intelligent and wearable devices are recommended for technical developers and enterprises. There is a great requirement to reduce CPU-GPU-TPU interaction, which costs resources, in order to provide smart decision support. Microcontroller makers should prioritize integrated TinyML design standards so that customers do not have to deal with external alignments linked to artificial intelligence. TinyML integration in the realm of IoT-edge analysis should be explored in order to make the application more user-friendly and trustworthy. To assist developers in implementing the market-ready development scenario, a uniform flow methodology should be designed. It is necessary to provide appropriate dataset repositories and lightweight benchmarking tools. TinyML adaptation should target X.0 industrial applications in the future days. Furthermore, such low-memory libraries should be used with 8-bit microcontroller devices. TinyML should be used to resolve latency mitigation in edge-level effects. TinyML is used in low-cost and portable digital devices to deliver immediate input to consumers. TinyML frameworks may be used to reduce needless utilization and reliance on GPUs, TPUs, and cloud platforms.