A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor

Abstract

Deep neural networks have recently become increasingly used for a wide range of applications, (e.g., image and video processing). The demand for edge inference is growing, especially in the areas of relevance to the Internet-of-Things. Low-cost microcontrollers as edge devices are a promising solution for optimal application systems from several points of view such as: cost, power consumption, latency, or real-time execution. The implementation of these systems has become feasible due to the advanced development of hardware architectures and DSP capabilities, while the cost and power consumption have been maintained at a low level. The aim of the paper is to provide a literature review on the implementation of deep neural networks using ARM Cortex-M core-based low-cost microcontrollers. As an emerging research direction, there are a limited number of publications that address this topic at the moment. Therefore, the research papers that stand out have been analyzed in greater detail, to promote further interest of researchers to bring AI techniques to low power standard ARM Cortex-M microcontrollers. The article addresses a niche research domain. Despite the increasing interest manifested toward both (1) edge AI applications and (2) theoretical contributions in DNN optimization and compression, the number of existing publications dedicated to the current topic is rather limited. Therefore, a comprehensive literature survey using systematic mapping is not possible. The presentation focuses on systems that have shown increased efficiency in resource-constrained applications, as well as the predominant impediments that still hinder their implementation. The reader will take away the following concepts from this paper: (1) an overview of applications, DNN architectures, and results obtained using ARM Cortex-M core-based microcontrollers, (2) an overview of low-cost hardware devices and SW development solutions, and (3) understanding recent trends and opportunities.

Deployment of Machine Learning Algorithms on Resource-Constrained Hardware Platforms for Prosthetics

“…TensorFlow Lite is an open-source framework to enable machine learning models on embedded systems. TensorFlow Lite was designed to provide a unified ML framework, addressing the multitude of embedded platforms and hardware support [10]. Therefore, the TensorFlow library can run with or without CMSIS support, so it is hardware independent.…”

“…This led several prosthetic control studies to be carried out outside lab settings, as well as many commercial applications like commercial prosthetics using advanced myoelectric algorithms [7]. For computational hardware in prosthetics, microcontrollers (MCU) are commonly favored over other embedded systems like Field Programmable Gate Arrays (FPGAs) or Application-Specific Integrated Circuits (ASICs) as the go-to platform because they are less complex to program and often feature extensive library and tool support [8]- [10]. Previously, standard machine learning algorithms like support vector machine (SVM) and linear discriminant analysis (LDA) were used on the computational hardware to decode motion intent [1].…”

Artificial Intelligence Techniques for Electronics

“…Low-cost microcontrollers are a promising solution in terms of their cost effectiveness. In the contribution by Orăs , an et al, "A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor", the authors conducted a study on the implementation of a deep neural network using an ARM Cortex-M core-based low-cost microcontroller [1]. On the other hand, there is a lot of interest in edge AI applications and DNN optimization and compression, but there are not many papers related to this research field.…”

Graph-Based Neural Networks’ Framework Using Microcontrollers for Energy-Efficient Traffic Forecasting

“…The main purpose of the article is to evaluate if neural network implementation (testing phase) is viable under the conditions of using limited resources (such as memory, calculation speed, and numerical precision) that a microcontroller has. From the literature, we can mention some papers that study the implementation of a neural network on microcontrollers such as the ARM microcontroller [8,9] and address the problem of finite precision of such microcontrollers [10].…”