Abstract
The convolutional neural network (CNN) models have proved to be very advantageous in computer vision and image processing applications. Recently, due to the increased accuracy of the CNNs on an extensive variety of classification and recognition tasks, the demand for real-time hardware implementations has dramatically increased. They involve intensive processing operations and memory bandwidth for achieving desired performance. The hardware resources and approximate performance estimation of a target system at a higher level of abstraction is very important for optimized hardware implementation. In this paper, initially we developed an ‘Optimized CNN model’, and then we explored the approximate operations and hardware resource estimation for this CNN model along with suitable hardware implementation process. We also compared the computed operations and hardware resource estimation of few published CNN architectures, which shows that optimization process highly helps in reducing the hardware resources along with providing a similar accuracy. This research has mainly focused on the computational complexity of the convolutional and fully connected layers of our implemented CNN model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT Press Cambridge (2016). https://www.deeplearningbook.org/
Nielsen, M.A.: Neural networks and deep learning. Determination Press (2015). http://neuralnetworksanddeeplearning.com/
LeCun, Y., Bottou, L., Benjio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
LeCun, Y., Cortes, C., Burges, C.: The MNIST database of handwritten digits (1999)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Hamid, N.A., Sjarif, N.N.: Handwritten Recognition Using SVM, KNN and Neural Networks (2017). arXiv, abs/1702.00723
Sun, W., Tseng, T.L.B., Zhang, J., Qian, W.: Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput. Med. Imaging Graph. 57, 4–9 (2017)
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhaocke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Chen, L., Wang, S., Fan, W., Sun, J., Naoi, S.: Beyond human recognition: a CNN-based framework for handwritten character recognition. In: 3rd IAPR Asian Conference on Pattern Recognition. Fujitsu Research and Development Center, Beijing, China (2015)
Agarap, A.F.: An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification (2019). arXiv:1712.03541v2 [cs.CV]
Acknowledgements
Our heartfelt appreciation to Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for the MNIST dataset (Lecun et al. 1999). This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pandey, J., Asati, A.R., Shenoy, M.V. (2022). Computational Operations and Hardware Resource Estimation in a Convolutional Neural Network Architecture. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision . Advances in Intelligent Systems and Computing, vol 1424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0475-2_17
Download citation
DOI: https://doi.org/10.1007/978-981-19-0475-2_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0474-5
Online ISBN: 978-981-19-0475-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)