A Dual-Branch Structure Network of Custom Computing for Multivariate Time Series

Abstract

Time series are a common form of data, which are of great importance in multiple fields. Multivariate time series whose relationship of dimension is indeterminacy are particularly common within these. For multivariate time series, we proposed a dual-branch structure model, composed of an attention branch and a convolution branch, respectively. The algorithm proposed in our work is implemented for custom computing optimization and deployed on the Xilinx Ultra 96V2 device. Comparative results with other state-of-the-art time series algorithms on public datasets indicate that the proposed method achieves optimal performance. The power consumption of the system is 6.38 W, which is 47.02 times lower than that of a GPU.