# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 2 Background
### EdgeML  
As mentioned on introducing the Edge Computing paradigm (Section 2.3), running ML models in the Cloud has severe counter indications, but, at the same time, running them on edge-side electronic devices is challenging due to the lack of sufficient computing resources. In the previous section, it was remarked that distributing the processing of ML models between the edge and the Cloud is the right strategy to overcome these issues. Figure 2: Two alternative implementations of AIoT. Today we observe a twofold convergent effort to improve the current state-of-the-art. On the industry side, there is a massive expansion of the market of hardware devices and complementary embedded software that can process ML algorithms, while academia is investigating new methods for pushing the processing of IoT data as close as possible to the IoT devices that the data come from. Two proposals by the industry are the following. Microsoft is developing EdgeML ([[https://www.microsoft.com/en-us/research/project/edgeml/](https://www.microsoft.com/en-us/research/project/edgeml/)]([https://www.microsoft.com/en-us/research/project/edgeml/](https://www.microsoft.com/en-us/research/project/edgeml/)) (accessed on 15 November 2024)), a suite of ML models that are trained on the Cloud and can run on resource-constrained edge IoT devices. The code of ML algorithms for edge devices developed at Microsoft Research India is available at the following repository [[https://github.com/Microsoft/EdgeML](https://github.com/Microsoft/EdgeML)]([https://github.com/Microsoft/EdgeML](https://github.com/Microsoft/EdgeML)). Google has released the LiteRT (short for Lite Runtime, formerly called TensorFlow Lite) platform. LiteRT features tools for converting TensorFlow Neural Network models into a simplified version that then can be run on edge-constrained devices. An interesting project from academia is the development of the Edge Learning Machine (ELM) framework. The latter aims at supporting developers in designing and deploying ML solutions on edge-constrained devices. In ELM, the ML model is created, trained, and optimized on a desktop computer, while inferences are run on MCUs. The framework implements three supervised ML algorithms (SVM, kNN, and DT); moreover, it supports ANNs leveraging the X-Cube-AI package for STM 32 devices [25]. The framework is open source and distributes a platform-independent C language implementation of those algorithms ([[https://github.com/Edge-Learning-Machine](https://github.com/Edge-Learning-Machine)]([https://github.com/Edge-Learning-Machine](https://github.com/Edge-Learning-Machine)) (accessed on 15 November 2024)). Another project from academia is the EdgeML framework described in [26]. It controls the execution of DNN models by combining a workload-offloading mechanism and dynamic neural architecture. To achieve good latency-accuracy-energy performance on edge-constrained IoT devices, EdgeML adopts the RL model. Authors implemented EdgeML for several well-known DNN models on the latest edge devices. EdgeML is developed on top of TensorFlow. The source code of EdgeML is available at [[https://github.com/Kyrie-Zhao/EdgeMLgit](https://github.com/Kyrie-Zhao/EdgeMLgit)]([https://github.com/Kyrie-Zhao/EdgeMLgit](https://github.com/Kyrie-Zhao/EdgeMLgit)) (accessed on 15 November 2024). In previous studies, the term EdgeAI (edge intelligence in [27]) has been largely used to refer to the scenario we are talking about, that is to signify that ML models are run either close to where the data are collected (for example, by leveraging MCUs connected to the IoT sensors), or in dedicated hardware (also called edge server or micro-data center) located near the IoT sensors. Ref. [28] is a recent work on EdgeAI. It reviews the hardware suitable for implementing EdgeAI, the supporting APIs, and the applications that can benefit from it. [PERSON] et al. [29] review the convergence of Edge Computing and AI for unmanned aerial vehicles, while [PERSON] et al.