I. Introduction
Near-edge computing systems can accelerate the real-time data processing from sensors in analog domains. Near-edge computing is emerging paradigm that can be used to address the problem of exponentially growing demand for sensory data processing. Neuromorphic systems is excellent candidate for edge computing for processing data in fast and efficient manner, due to high operational similarity with biological neural systems. Neuro-fuzzy systems (NFS), relaxed rule-based implementation of human-like soft decision making, find many applications in data driven control systems and some in pattern classification [1], [2]. This low popularity of NFS in the field of large scaled data driven pattern recognition is attributed to the exponential system complexity growth related to the growth of input data size [3]. The scalable and efficient implementation of fully operational NFS for neuromorphic applications is still an open problem. Analog computing circuits are solution for this issue [4].