A spiking neural network chip for odor data classification

An artificial nose, also known as an “electronic nose” (E-Nose), has found many applications. One of the restrictions for E-Nose becoming popular is its size and power consumption. To reduce the power consumption and physical size of an E-Nose system, a power-efficient odor data classification chip is advantageous. This paper presents a low-power, neuromorphic spiking neural network chip which can be integrated in an electronic nose system to perform odor data classification. The network is composed of integrate-and-fire neurons, using spike-timing dependent plasticity for learning. The network has been fabricated by TSMC 0.18 μm CMOS process. The chip area is 1.033×1.383 mm². Measurement results show that the chip can correctly classify real world gas data (hami and lemon) sampled by the commercial E-Nose, Cyranose 320. The supply voltage is 1.2 V; the power consumption is 3.6 μW. This learning chip features small area, low voltage and low power, and is very suitable for being integrated in an E-Nose system. The power and size of the E-Nose can be reduced and have more extensive applications.