Fast-digital multiplication using multizero neural networks
Chia-Lun John Hu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
The multi-zero artificial neural network was derived from a study of the stability and convergence properties of a feedback (or auto-associative) neural system. The nonlinear response function of neurons in the system is an odd polynomial (or a topologically similar) function of 2M + 1 zeros with odd zeros equal to a set of consecutive integers. If the connection matrix is programmed correctly, the system will then perform stable operations exhibiting the following characteristics: (1) The system will transform any N-bit analog input to an N-bit, M-ary (or M-valued), digital output. (2) The output will be locked in when the input is removed. It will be changed to another locked in digital vector when it receives another input. (3) The speed is fast because the circuit is free-running, parallel, and M-ary. The accuracy is high because the computation is digital. Because of these unique properties, the network can be used in the design of a fast computing system. This paper reports the origin of this multizero system, the analysis of its properties, and the design of a fast, M-ary, digital multiplier using this system.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.