FPGA architecture for feed-forward sequential memory network targeting long-term time-series forecasting

Abstract

Deep learning is being widely used in various applications, and diverse neural networks have been proposed. A form of neural network, such as the novel feed-forward sequential memory network (FSMN), aims to forecast prospective data by extracting the time-series feature. FSMN is a standard feed-forward neural network equipped with time-domain filters, and it can forecast without recurrent feedback. In this paper, we propose a field-programmable gate-array (FPGA) architecture for this model, and exhibit that the resource does not increase exponentially as the network scale increases.