A novel pipeline architecture of replacing Ink Drop Spread

Human Brain is one of the most wonderful and complex systems which is designed for ever; A huge complex network composed of neurons as tiny biological and chemical processors which are distributed and work together as a super parallel system to do control and vital activities of human body. Brain learning simulation and hardware implementation is one of the most interesting research areas in order to make artificial brain. One of the researches in this area is Active Learning Method in brief ALM. ALM is an adaptive recursive fuzzy learning algorithm based on brain functionality and specification which models a complex Multi Input Multi Output System as a fuzzy combination of Single Input Single Output Systems. ALM has a basic operator which is called Ink Drop Spread in brief IDS; this operator has a learning granularity concept to knowledge resolution tuning through learning process. Also IDS serves as processing engine for ALM which extracts main features of system subjected to modeling. Because of matrix like architecture of IDS, it is memory hungry and makes ALM slow. So an arithmetical form of IDS called Replacing IDS is presented in this paper with same functionality and better performance. Also in this paper we present a novel pipeline digital architecture of RIDS with high throughput and high speed learning process and simple hardware structure with more flexibility and scalability in comparison with another architectures. Finally an application benchmark is used for validation process.