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Versatile Architectures of Artificial Neural Network with Variable Capacity

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Abstract

Artificial neural network (ANN) is widely used in modern engineering applications. The decision on the number of layers and the number of nodes per layer in the ANN or the capacity of the ANN is always a non-trivial. The wrong decision on the capacity of the ANN causes underfit or overfit. This paper proposes various versatile or flexible hardware architectures of multilayer perceptron (MLP)-based neural network, where the number of layers and the number of nodes per layer can be changed with respect to the requirement of the application by avoiding underfit or overfit. Also, the weights of the each node of the MLP can be fixed by the training phase. While the network has being trained, we can change the architecture of the MLP without affecting the accuracy. All the proposed and existing hardware designs of ANNs are implemented with 45 nm CMOS technology. The proposed high throughput design with 3 layers and 512 nodes per layer achieves \(53.8\%\) of improvement in the throughput as compared with the existing technique.

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  1. Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kurnool, Andhra Pradesh, 518007, India

    M. Mohamed Asan Basiri

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MA Basiri M contributed to conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision, project administration, and funding acquisition. The author has read and agreed to the published version of the manuscript.

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Correspondence to M. Mohamed Asan Basiri.

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Basiri, M.M.A. Versatile Architectures of Artificial Neural Network with Variable Capacity. Circuits Syst Signal Process 41, 6333–6353 (2022). https://doi.org/10.1007/s00034-022-02087-3

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