Neuromorphic Hardware Accelerated Adaptive Authentication System

In this paper we present a multimodal authentication (person identification) system based on simultaneous recognition of face and speech data using a novel bio-inspired architecture powered by the CM1K chip. The CM1K chip has a constant recognition time irrespective of the size of the knowledge base, which gives massive time gains in learning and recognition over software implementations of similar methods. We demonstrate a system utilizing the CM1K chip as a neural network accelerator along with data pre-processing done by a desktop PC. The system realized consumes energy of the order: 668 mu J for learning and 487 mu J for recognition, while operating at 25 MHz. The classification test accuracy of the system is approximately 91%.