Performance Analysis of Convolutional Neural Networks on Embedded Systems

Grzymkowski, Lukasz; Stefański, Tomasz P.

doi:10.23919/mixdes49814.2020.9155741

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…b) Optimising DL inference on MCUs: Related to this category, Grzymkowski et al [19] performed a performance analysis of CNNs deployed on an NXP i.MX RT1050 development board equipped with an ARM Cortex-M7 core. They investigated the impact of various factors, including core frequency, memory access and DSP instruction usage, using TensorFlow Lite and CMSIS-NN as the inference engines.…”

Section: A Backgroundmentioning

confidence: 99%

Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors

Maciá-Lillo,

Barrachina,

Fabregat

et al. 2024

IEEE Internet Things J.

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We perform a series of optimisations on the convolution operator within the ARM CMSIS-NN library to improve the performance of deep learning tasks on Arduino development boards equipped with ARM Cortex-M4 and M7 microcontrollers. To this end, we develop custom microkernels that efficiently handle the internal computations required by the convolution operator via the lowering approach and the direct method, and we design two techniques to avoid register spilling. We also take advantage of all the RAM on the Arduino boards by reusing it as a scratchpad for the convolution filters. The integration of these techniques into CMSIS-NN, when invoked by TensorFlow Lite for microcontrollers for quantised versions of VGG, SqueezeNet, ResNet, and MobileNet-like convolutional neural networks enhances the overall inference speed by a factor ranging from 1.13× to 1.50×.

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Section: A Backgroundmentioning

confidence: 99%

Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors

Maciá-Lillo,

Barrachina,

Fabregat

et al. 2024

IEEE Internet Things J.

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Facial Image-based Age and Gender Classification to Enhance Recommendations in a Smart TV Environment

Abirami

Anand

Bhaskaran

2023

2023 International Conference on Networking and Communications (ICNWC)

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

2022

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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.

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Performance Analysis of Convolutional Neural Networks on Embedded Systems

Cited by 3 publications

References 5 publications

Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors

Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors

Facial Image-based Age and Gender Classification to Enhance Recommendations in a Smart TV Environment

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

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