Publication Type : Conference Proceedings
Publisher : 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology
Source : 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT) (2018)
Url : https://ieeexplore.ieee.org/document/9012325
Keywords : Compressors,Adders,Artificial neural networks,Logic gates,Hardware,Computational modeling,System-on-chip,Approximate computing,exact multipliers
,compressors,low power,neural network
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2018
Abstract : Approximate computing is a positive approach for energy efficient design of digital systems. This is an attractive archetype for approximation in multipliers that deals for a new design approach. For error resilient application, there is an increase in performance and power efficiency. This type of computing is particularly interested for computer arithmetic design. The multipliers are designed by introducing various probability terms and partial products. The design complexity is decreased with this type of multiplier. This work deals with the analysis and design of 4-bit and 8-bit exact and approximate multipliers. 4-2 compressors are used to modify the architecture and utilized in the proposed multiplier. This methodology was synthesized using Xilinx ISE. The hardware implementation is carried on ZYBO Zynq-7000 development board. The total power reduction for the approximate multiplier is 8% for 4-bit and 17.3% for 8-bit. This is much more efficient than exact multiplier design. The designed multiplier was tested on a 3 layer artificial neural network (ANN) model and reduction in power was obtained.
Cite this Research Publication : K. G. Hemamithra, S Priya, L., Lakshmirajan, K., Mohanrai, R., and Ramesh S. R., “FPGA Implementation of Power Efficient Approximate Multipliers”, 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT). 2018.