title: VLSI neural system architecture for finite ring recursive reduction.
abstract: The use of neural-like networks to implement finite ring computations has been presented in a previous paper. This paper develops efficient VLSI neural system architecture for the finite ring recursive reduction (FRRR), including module reduction, MSB carry iteration and feedforward processing. These techniques deal with the basic principles involved in constructing a FRRR, and their implementations are efficiently matched to the VLSI medium. Compared with the other structure models for finite ring computation (e.g. modification of binary arithmetic logic and bit-steered ROM's), the FRRR structure has the lowest area complexity in silicon while maintaining a high throughput rate. Examples of several implementations are used to illustrate the effectiveness of the FRRR architecture.
authors.affiliation: Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong. dapeng@cs.cityu.edu.hk
authors.forename: D
authors.initials: D
authors.lastname: Zhang
authors.forename: G A
authors.initials: GA
authors.lastname: Jullien
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003199:Computer Systems; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways; D012815:Signal Processing, Computer-Assisted