Cerebellar model arithmetic computer with pseudo‐bacterial genetic algorithm and its hardware acceleration

Abstract

SUMMARYThe cerebellar model arithmetic computer (CMAC) is a kind of neural network that is capable of high-speed learning. In the use of the CMAC, various parameters must be defined, but it is difficult to set the parameters adequately. The pseudo-bacterial genetic algorithm (PBGA) is a kind of evolutionary computation which offers local search. This paper considers adequate determination of the location of the boundary line, which is one of the parameters of the CMAC. For that purpose the PBGA is applied, and PBGA/CMAC, which realizes the efficient search of the boundary line of the CMAC, is proposed. Furthermore, since PBGA/CMAC requires a large amount of computation, a hardware implementation is considered in order to improve the computation speed. The computation involved in the learning rules of the CMAC is divided into four stages, and the structure is implemented as a pipeline. The pipeline implementation produces read-after-write hazards for the granular cell values. However, it is shown that the difference in performance from conventional CMAC learning rule is small, indicating the possibility of achieving high-speed learning. The speed of PBGA/CMAC hardware is improved to 140 times the speed of operation by software. In order to present a guide for PBGA/CMAC hardware, the hardware volume is evaluated, and it is shown that it can be implemented with precision sufficient for practice.

Fuzzy CMAC structures

“…CMAC is actually an acronym for Cerebellum Model Articulation Controller applied in robot arm control but it is used in other areas and sometimes is known as Cerebellar Model Arithmetic Computer [4]- [6]. CMAC model is shown in many forms.…”