期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 23, 期 7, 页码 1065-1073出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2012.2195329
关键词
Analog very-large-scale integration (VLSI); electronic nose; olfaction; spiking neural network; subthreshold oscillation
类别
资金
- National Science Council (NSC) of Taiwan [NSC 100-2220-E-007-007]
This paper presents a low-power, neuromorphic spiking neural network (SNN) chip that can be integrated in an electronic nose system to classify odor. The proposed SNN takes advantage of sub-threshold oscillation and onset-latency representation to reduce power consumption and chip area, providing a more distinct output for each odor input. The synaptic weights between the mitral and cortical cells are modified according to an spike-timing-dependent plasticity learning rule. During the experiment, the odor data are sampled by a commercial electronic nose (Cyranose 320) and are normalized before training and testing to ensure that the classification result is only caused by learning. Measurement results show that the circuit only consumed an average power of approximately 3.6 mu W with a 1-V power supply to discriminate odor data. The SNN has either a high or low output response for a given input odor, making it easy to determine whether the circuit has made the correct decision. The measurement result of the SNN chip and some well-known algorithms (support vector machine and the K-nearest neighbor program) is compared to demonstrate the classification performance of the proposed SNN chip. The mean testing accuracy is 87.59% for the data used in this paper.
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