# Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated
## 1 Introduction
This sensor system allowed a large amount of data to be stored during the use of electric vehicles and then made it available for the training of data-driven techniques [25]. However, it is important to remind that the computational capacity of embedded systems deployed in EVs is limited, while the goal of estimating SoC under short latencies and accurately is of paramount and practical importance. Numerous optimisation techniques have recently been developed to enable the successful deployment of ANN on embedded devices, such as microcontrollers. For instance, 8-bit quantisation allowed the memory footprint of the model to be considerably reduced to a fourth compared to floating point 32 bits (fp32), making possible to combine next-generation edge devices with Artificial Intelligence (AI) capabilities [26]. Technological progress in the low cost hardware components of BMS, such as sensors to measure physical properties of interest and microcontrollers (MCUs) to process signals [27], have made the application of edge AI techniques viable [28]. AI algorithms deployed to MCU make it possible to perform SoC inference, guaranteeing great accuracy to its estimation. Edge AI avoids the use of cloud resources to run AI models, increasing privacy, scalability and by reducing power consumption, memory and latency without the data leaving the edge device where they are collected to feed ML inference workloads. A central feature of edge computing is that computation is moved to the edge of the internet of things (IoT) network within resource-constrained embedded devices that are not equipped to run computationally intensive tasks as the cloud does [29]. In recent years there has been growing attention towards the field of tiny machine learning (TinyML), which aims to combine the powerful inferential capabilities of ANN with the computational efficiency of ultra-low power embedded devices [30]. The cornerstone of TinyML is to reduce the number of ANN's parameters to the minimum without drastically compromising their performance, so as to obtain accurate results with much lower latency than cloud computing. The TinyML foundation [31] was established in 2019 to federated a community easing the adoption of ML approaches at the edge on resource limited low power (under 1 mW) embedded devices, such as MCU. Therefore, this research focused on the study of tiny ANNs in resource-constrained devices. This paper is structured as follows. In Section 3, the specific challenge guiding this research is defined. Subsequently, in Section 2, a review of the existing literature is performed. In Section 4, the contribution to the field is presented; Section 5 describes the dataset which shapes the problem under consideration; Section 6 describes the AI topologies subject of accuracy investigations by this paper; Sections 7 and 9 are dedicated to the experimental and deployability analysis, respectively; Section 12 is dedicated to study computational complexity and inference time required to compute SoC for each cell of the battery pack; Section 13 lists main findings gathered by this work and proposes some future developments.