An efficient hardware design for cerebellar models using approximate circuits

Abstract

The superior controllability of the cerebellum has motivated extensive interest in the development of computational cerebellar models. Many models have been applied to the motor control and image stabilization in robots. Often computationally complex, cerebellar models have rarely been implemented in dedicated hardware. Here, we propose an efficient hardware design for cerebellar models using approximate circuits with a small area and a low power. Leveraging the inherent error tolerance in the cerebellum, approximate adders and multipliers are carefully evaluated for implementations in an adaptive filter based cerebellar model to achieve a good tradeoff in accuracy and hardware usage. A saccade system, whose vestibulo-ocular reflex (VOR) is controlled by the cerebellum, is simulated to show the applicability and effectiveness of the proposed design. Simulation results show that the approximate cerebellar circuit achieves a similar accuracy as an exact implementation, but it saves area by 29.7% and power by 37.3%.