Neuron-MOS-based association hardware for real-time event recognition

Abstract

Neuron MOS transistor (/spl upsi/MOS) mimicking the fundamental behavior of neurons at a very primitive device level has been applied to construct a real-time event recognition hardware. A neuron MOS associator searches for the most similar event in the past memory to the current event based on Manhattan distance calculation and the minimum distance search by a winner take all (WTA) circuitry in a fully parallel architecture. A unique floating-gate analog EEPROM technology has been developed to build a vast memory system storing the events in the past. Test circuits of key subsystems were fabricated by a double-polysilicon CMOS process and their operation was verified by measurements as well as by simulation. As a simple application of the basic architecture, a motion-vector-search hardware was designed and fabricated. The circuit can find out the two-dimensional motion vector in about 150 nsec by a very simple circuitry.