Study of 1-bit data stream digital circuit of neural network for higher order chemical sensing

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Various types of LSI for neural network have been so far studied by many institutions. We proposed a new architecture of digital neural network circuit based upon 1-bit data stream signal processing circuit. Furthermore, we aimed to realize LSI using that method and to apply it to information processing of chemical sensors. The circuit for learning and recognizing an odor using the output pattern of quartz-resonator gas sensors was implemented into LSI fabricated utilizing the chip fabrication service. The neural network of LVQ was realized using 24000 transistors including measurement circuits. We confirmed that the circuit correctly behaved using the data of those patterns obtained by the off-line measurements. Furthermore, the circuit with 116,000 transistors including self-test circuit, computer interface was designed. Since the full custom design of the memory circuit was performed, the layout method was building-block one. The chip will be evaluated in the near future. On the other hand, the circuit implemented into FPGA was used to do the experiment on the identification of the odors with the same number of carbons but with the different functional groups using the simple measurement system for a portable odor sensing system. It was found that the odors were correctly identified even when the on-line identification was performed. Furthermore, 1-bit data stream signal processing circuit was applied to the direction estimation of the gas flow using the sensor-array signals. We confirmed that the circuit solved the linear equations based upon the optical-flow constraints when the off-line data was input to the circuit implemented into FPGA.