FPGA-based Learning Acceleration for LSTM Neural Network

Abstract

This paper presents and discusses the implementation of a learning accelerator for an LSTM neural network that utilizes an FPGA. The accelerator consists of a backpropagation through time algorithm for an LSTM. The presented net performs a binary classification task and consists of an LSTM and a dense layer. The performance is then compared to both a hard-coded Python implementation and an implementation using Keras library and the GPU. The implementation is executed using the DSP blocks, available via the Vivado Design Suite, which is in compliance with the IEEE754 standard. The results of the simulation show that the FPGA implementation remains accurate and achieves higher speed than the other solutions.