Title :
Efficient diminished-1 modulo 2n+1 multiplier architectures
Author :
Xiaolan Lv ; Ruohe Yao
Author_Institution :
Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
The main components of an artificial neuron are adders and multipliers. In order to implement neural network, large number of adders and multipliers are required. The efficient architectures for diminished-1 modulo 2n+1 multipliers are described. The results and operands of the new modulo 2n+1 multipliers use the diminished-1, avoiding n+1 bit circuit. And the presented multipliers can handle zero inputs and results. The proposed modulo 2n +1 multiplier are built using three major functional modules, partial products generation block, partial products reduction block and a final diminished-1 adder block. The final modulo 2n +1 addition block is built around a sparse carry computation unit for the analytical and experimental results. And this indicates that the significant area and power of the proposed multipliers is superior to the earlier proposals, with a high operation speed.
Keywords :
adders; multiplying circuits; neural nets; adders; artificial neuron; diminished-1 adder block; diminished-1 modulo 2n+1 multiplier architectures; functional modules; multipliers; neural network; operands; partial products generation block; partial products reduction block; sparse carry computation unit; Adders; Arrays; Biological neural networks; Delays; Logic gates; Vectors; Diminished-1 representation; Residue number system (RNS); VLSI; modular multiplier;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889540