Abstract
This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning algorithms (Support Vector Machine (SVM) with a linear kernel, k-Nearest Neighbors (K-NN), and Decision Tree (DT)), and exploits STM X-Cube-AI to implement Artificial Neural Networks (ANNs) on STM32 Nucleo boards. We investigated the performance of these algorithms on six embedded boards and six datasets (four classifications and two regression). Our analysis—which aims to plug a gap in the literature—shows that the target platforms allow us to achieve the same performance score as a desktop machine, with a similar time latency. ANN performs better than the other algorithms in most cases, with no difference among the target devices. We observed that increasing the depth of an NN improves performance, up to a saturation level. k-NN performs similarly to ANN and, in one case, even better, but requires all the training sets to be kept in the inference phase, posing a significant memory demand, which can be afforded only by high-end edge devices. DT performance has a larger variance across datasets. In general, several factors impact performance in different ways across datasets. This highlights the importance of a framework like ELM, which is able to train and compare different algorithms. To support the developer community, ELM is released on an open-source basis.
Highlights
The trend of moving computation towards the edge is becoming ever more relevant, leading to performance improvements and the development of new field data processing applications [1].This computation shift from the cloud (e.g., [2]) to the edge has advantages in terms of response latency, bandwidth occupancy, energy consumption, security and expected privacy (e.g., [3])
For Support Vector Machine (SVM), k-Neural Network (NN) and Decision Tree (DT) on desktops, we report the performance of both our C implementation and the python scikit-learn implementation, while for Artificial Neural Networks (ANNs) we have only the TensorFlow Keras implementation
We considered three cases that we applied on ANN, SVM, and k-Nearest Neighbors (k-NN): no scaling, MinMax Scaler, and Standard Scaler (Std) [54]
Summary
The trend of moving computation towards the edge is becoming ever more relevant, leading to performance improvements and the development of new field data processing applications [1]. While the literature is increasingly reporting on novel or adapted embedded machine learning algorithms, architectures and applications, there is a lack of quantitative analyses about the performance of common ML algorithms on state-of-the-art mainstream edge devices, such as ARM microcontrollers [10]. We argue that this has limited the development of new applications and the upgrading of existing ones through an edge computing extension.
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