K-Means algorithm relies on a nearest distance metric for the calculations of the cluster convergence to a local

This paper reports a hardware-accelerated multi-prototype learning and classification system which is suitable for real-time recognition systems. The real-world applicability of robotics or surveillance systems is dependent upon their real-time performance. Hardware based solutions can meet the needs for real-time limited problems; however, hardware-friendly solutions have lacked the flexibility to handle a large range of complex tasks. Software based solutions have been used to tackle complex tasks and allow for greater flexibility but lack the speeds which hardware systems can provide. The developed multi-prototype learning and classification system surmounts these limitations and is applied to the problem of human recognition for demonstrating its capabilities. A fully digital Euclidian distance searching circuit is developed in order to reduce the computational cost within the learning and classification process. The system outperforms other implementations by significantly reducing training times and attains a per sample recognition speed of 1.03 μs.