FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network.

ABSTRACT: The integrated electronic nose (e-nose) design, which integrates sensor arrays and recognition algorithms, has been widely used in different fields. However, the current integrated e-nose system usually suffers from the problem of low accuracy with simple algorithm structure and slow speed with complex algorithm structure. In this article, we propose a method for implementing a deep neural network for odor identification in a small-scale Field-Programmable Gate Array (FPGA). First, a lightweight odor identification with depthwise separable convolutional neural network (OI-DSCNN) is proposed to reduce parameters and accelerate hardware implementation performance. Next, the OI-DSCNN is implemented in a Zynq-7020 SoC chip based on the quantization method, namely, the saturation-flooring KL di

SUBMITTER: Mo Z 

PROVIDER: S-EPMC7865880 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature