NEW APPROACH OF SIGNED BINARY NUMBERS MULTIPLICATION AND ITS IMPLEMENTATION ON FPGA

Sarifuddin Madenda
Universitas Gunadarma
Indonesia
Suryadi Harmanto
Universitas Gunadarma
Indonesia

Abstract

This paper proposes a new model of signed binary multiplication. This model is formulated mathematically and can handle four types of binary multipliers: signed positive numbers multiplied by signed positive numbers (SPN-by-SPN); signed positive numbers multiplied by signed negative numbers (SPN-by-SNN); signed negative numbers multiplied by signed positive numbers (SNN-by-SPN); and signed negative numbers multiplied by signed negative numbers (SNN-by-SNN). The proposed model has a low complexity algorithm, is easy to implement in software coding and integrated in a hardware FPGA (Field-Programmable Gate Array), and is more powerful compared to the modified Baugh-Wooley's model.

Keywords
Signed Binary Numbers; Signed Multiplication; Algorithm; Multiplier Circuit; FPGA
References

Y. Chen, S. Duffner, A. Stoian, J.-Y. Dufour, A. Baskurta, “Deep and low-level feature based attribute learning for person re-identification,” Image Vis. Comput., vol. 79. pp 25–34, 2018.

X. Cheng, J. Lu, J. Feng, B. Yuan, J. Zhou, “Scene recognition with objectness, Pattern Recognition,” vol. 74. pp 474–487, 2018.

J. Zhang, K. Shao, X. Luo, “Small sample image recognition using improved Convolutional Neural Network,” J. Vis. Commun. Image Represent. Vol. 55, pp 640–647, 2018.

S.S. Sarikan, A.M. Ozbayoglu, O. Zilcia, Automated vehicle classification with image processing and computational intelligence, Procedia Comput. Sci. vol. 114, 515–522, 2017.

A. Qayyum, S.M. Anwar, M. Awais, M. Majid, “Medical image retrieval using deep convolutional neural network,” Neurocomputing, vol. 266, pp 8–20, 2017.

L. Gong, C. Wang, X. Li, H. Chen, X. Zhou, “MALOC: A fully pipelined FPGA accelerator for convolutional neural networks with all layers mapped on chip,” IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. Vol. 37, no. 11, pp 2601–2612, 2018.

N.I. Chervyakov, P.A. Lyakhov, M.V. Valueva, “Increasing of Convolutional Neural Network performance using residue number system,” International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), pp. 135–140, 2017.

A. Shawahna, S.M. Sait, A. El-Maleh, “FPGA-based accelerators of deep learning networks for learning and classification: A review,” IEEE Access, vol. 7, 7823–7859, 2019.

H. Sim and J. Lee, "A New Stochastic Computing Multiplier with Application to Deep Convolutional Neural Networks", 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC), Jun. 2017.

Juan Renteria-Cedano 1 , Jorge Rivera 2,* , F. Sandoval-Ibarra 1 , Susana Ortega-Cisneros 1 and Raúl Loo-Yau 1, SoC Design Based on a FPGA for a Configurable Neural Network Trained by Means of an EKF, Electronics 2019, 8, 761; doi:10.3390/electronics8070761 www.mdpi.com/journal/electronics

FPGA Acceleration of Matrix Multiplication for Neural Networks (xilinx.com) XAPP1332 (v1.0) February 27, 2020 www.xilinx.com Application Note.

Baugh C.R., Wooley B.A., A Two’s Complement Parallel Array Multiplication Algorithm. IEEE Trans. Comput. C-22, pp 1045–1047, 1973.

PramodiniMohanty, RashmiRanjan, “An Efficient Baugh Wooley Architecture for Both Signed & Unsigned Multiplication”, International Journal of Computer Science and Engineering Technology, vol. 3, no. 4, April 2012.

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