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

机译:基于神经元-MOS的关联硬件,用于实时事件识别

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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.
机译:已经应用了MIMICING在非常原始的设备级别的神经元的基本行为的神经元MOS晶体管(/ SPL UPSI / MOS)构建了实时事件识别硬件。神经元MOS主人在过去存储器中搜索最常见的事件,以基于曼哈顿距离计算的当前事件,并且获奖者的最小距离搜索在完全并行架构中取出所有(WTA)电路。已经开发出独特的浮动门模拟EEPROM技术,以构建庞大的内存系统,存储过去的事件。通过双重多晶硅CMOS工艺制造关键子系统的测试电路,并且通过测量以及模拟验证它们的操作。作为基本架构的简单应用,设计和制造了运动矢量搜索硬件。电路可以通过非常简单的电路在大约150 nsec中找到二维运动矢量。

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