A Survey on Neural Network Hardware Accelerators

Mohaidat, Tamador; Khalil, Kasem

doi:10.1109/tai.2024.3377147

Cited by 11 publications

(1 citation statement)

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“…Several practical applications of efficient CNNs have been investigated using various types of hardware architectures and platforms. In [12], the hardware implementation of different complex machine learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and artificial neural network (ANNs) is investigated. The challenges, such as speed, area, resource consumption, and throughput, are discussed.…”

Section: Related Workmentioning

confidence: 99%

Wind Power Forecasting with Machine Learning Algorithms in Low-Cost Devices

Buestán-Andrade,

Peñacoba-Yagüe,

Sierra-García

et al. 2024

Electronics

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The urgent imperative to mitigate carbon dioxide (CO2) emissions from power generation poses a pressing challenge for contemporary society. In response, there is a critical need to intensify efforts to improve the efficiency of clean energy sources and expand their use, including wind energy. Within this field, it is necessary to address the variability inherent to the wind resource with the application of prediction methodologies that allow production to be managed. At the same time, to extend its use, this clean energy should be made accessible to everyone, including on a small scale, boosting devices that are affordable for individuals, such as Raspberry and other low-cost hardware platforms. This study is designed to evaluate the effectiveness of various machine learning (ML) algorithms, with special emphasis on deep learning models, in accurately forecasting the power output of wind turbines. Specifically, this research deals with convolutional neural networks (CNN), fully connected networks (FC), gated recurrent unit cells (GRU), and transformer-based models. However, the main objective of this work is to analyze the feasibility of deploying these architectures on various computing platforms, comparing their performance both on conventional computing systems and on other lower-cost alternatives, such as Raspberry Pi 3, in order to make them more accessible for the management of this energy generation. Through training and a rigorous benchmarking process, considering accuracy, real-time performance, and energy consumption, this study identifies the optimal technique to accurately model such real-time series data related to wind energy production, and evaluates the hardware implementation of the studied models. Importantly, our findings demonstrate that effective wind power forecasting can be achieved on low-cost hardware platforms, highlighting the potential for widespread adoption and the personal management of wind power generation, thus representing a fundamental step towards the democratization of clean energy technologies.

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Section: Related Workmentioning

confidence: 99%