VLSI Architecture for Matrix Multiplier using Parallel To Parallel Input Multiple Output Technique

Authors

  • Rajesh Yadav, Prof. Satyarth Tiwari

Keywords:

Matrix Multiplication, Parallel to Parallel Input Multiple Output (PPI-MO), Round based Approximated Multipliers (ROAM)

Abstract

In the present scenario, the rapid growth of wireless communication, multimedia applications, robotics and graphics increases the demand for resource efficient, high throughput and low power digital signal processing (DSP) systems. Matrix multiplication (MM) is the most widely used fundamental processing element in almost all DSP systems ranging from audio/video signal processing to wireless sensor networks. Hardware implementation of MM requires a huge number of arithmetic operations that affect the speed and consumes more area and power. Pipelining and parallel processing are the two methods used in the DSP systems to reduce the area. MM is the kernel operation used in many transform, image and discrete signal processing application. We develop new algorithms and new techniques for MM on configurable devices. In this paper, we have proposed MM using round based approximated multipliers. This design reduced hardware complexity, delay and input/output data format to match different application needs. The PPI-MO based MM is design Xilinx software and simulated number of slice, look up table and delay.

References

Chen Yang, Siwei Xiang, Jiaxing Wang, and Liyan Liang, “A High Performance and Full Utilization Hardware Implementation of Floating Point Arithmetic Units,” 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), IEEE, 2021.

Rongyu Ding, Yi Guo, Heming Sun, and Shinji Kimura, “Energy-Efficient Approximate Floating Point Multiplier Based on Radix-8 Booth Encoding,” IEEE 14th International Conference on ASIC (ASICON), IEEE, 2021.

Wei Mao, Kai Li, Xinang Xie, Shirui Zhao, He Li, and Hao Yu, “A Reconfigurable Multiple-Precision Floating Point Dot Product Unit for High-Performance Computing,” Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, 2021.

Rahul Rathod, P. Ramesh, Pratik S. Zele, and Annapurna K. Y., “Implementation of Complex Floating Point Multiplier Using 32-Bit Vedic Multiplier, Array Multiplier and Combined Integer and Floating Point Multiplier (CIFM),” International Conference on Innovation in Technology (INOCON), IEEE, 2020.

S. Ross Thompson and James E. Stine, “A Novel Rounding Algorithm for a High Performance IEEE 754 Double-Precision Floating-Point Multiplier,” 38th International Conference on Computer Design (ICCD), IEEE, 2020.

P. L. Lahari, M. Bharathi, and Yasha Jyothi M. Shirur, “High Speed Floating Point Multiply Accumulate Unit using Offset Binary Coding,” 7th International Conference on Smart Structures and Systems (ICSSS), IEEE, 2020.

Lakshmi Kiran Mukkara and K. Venkata Ramanaiah, “A Simple Novel Floating Point Matrix Multiplier VLSI Architecture for Digital Image Compression Applications,” 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT), IEEE, 2018.

Soumya Havaldar and K. S. Gurumurthy, “Design of Vedic IEEE 754 Floating Point Multiplier,” IEEE International Conference on Recent Trends in Electronics Information Communication Technology, India, May 2016.

Ragini Parte and Jitendra Jain, “Analysis of Effects of using Exponent Adders in IEEE-754 Multiplier by VHDL,” International Conference on Circuit, Power and Computing Technologies (ICCPCT), IEEE, 2015.

Ross Thompson and James E. Stine, “An IEEE 754 Double-Precision Floating-Point Multiplier for Denormalized and Normalized Floating-Point Numbers,” International Conference on IEEE, 2015.

M. K. Jaiswal and R. C. C. Cheung, “High Performance FPGA Implementation of Double Precision Floating Point Adder/Subtractor,” International Journal of Hybrid Information Technology, vol. 4, no. 4, Oct. 2011.

B. Fagin and C. Renard, “Field Programmable Gate Arrays and Floating Point Arithmetic,” IEEE Transactions on VLSI, vol. 2, no. 3, pp. 365–367, 1994.

N. Shirazi, A. Walters, and P. Athanas, “Quantitative Analysis of Floating Point Arithmetic on FPGA Based Custom Computing Machines,” Proceedings of the IEEE Symposium on FPGAs for Custom Computing Machines (FCCM’95), pp. 155–162, 1995.

Malik and S.-B. Ko, “A Study on the Floating-Point Adder in FPGAs,” Canadian Conference on Electrical and Computer Engineering (CCECE-06), pp. 86–89, May 2006.

D. Sangwan and M. K. Yadav, “Design and Implementation of Adder/Subtractor and Multiplication Units for Floating-Point Arithmetic,” International Journal of Electronics Engineering, pp. 197–203, 2010.

L. Louca, T. A. Cook, and W. H. Johnson, “Implementation of IEEE Single Precision Floating Point Addition and Multiplication on FPGAs,” Proceedings of 83rd IEEE Symposium on FPGAs for Custom Computing Machines (FCCM’96), pp. 107–116, 1996.

Jaenicke and W. Luk, “Parameterized Floating-Point Arithmetic on FPGAs,” Proc. of IEEE ICASSP, vol. 2, pp. 897–900, 2001.

Lee and N. Burgess, “Parameterisable Floating-point Operations on FPGA,” Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems, and Computers, 2002.

M. Al-Ashrafy, A. Salem, and W. Anis, “An Efficient Implementation of Floating Point Multiplier,” Saudi International Electronics, Communications and Photonics Conference (SIECPC), pp. 1–5, Apr. 2011.

Downloads

How to Cite

Rajesh Yadav, Prof. Satyarth Tiwari. (2024). VLSI Architecture for Matrix Multiplier using Parallel To Parallel Input Multiple Output Technique. International Journal of Research & Technology, 12(1), 20–24. Retrieved from https://ijrt.org/j/article/view/237

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.