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Recently, artificial neural networks and neuro-fuzzy systems are being introduced in embedded systems due to often-used solution for classification and nonlinear system identification. In this paper, we present a parametric neuro-fuzzy hardware and a framework for exploring design space for an efficient hardware realization of neuro-fuzzy models for embedded systems. The proposed hardware can be used as a stand-alone core or be coupled with a central processing unit for the purpose of online training. We also present a framework to explore the design space for optimal design parameters (hardware core parameters) so that an efficient neuro-fuzzy hardware in terms of area, power consumption, and performance (delay) can be selected. It examines whole design space to find Pareto efficient hardware without increasing time-to-market and non-recurring engineering cost with the aid of high-level design space …