An FPGA implementation of a long short term memory neural network
Our work proposes a hardware architecture for a Long Short Term Memory (LSTM) Neural Network, aiming to outperform software implementations, by exploiting its inherent parallelism. The main design decisions are presented, along with the proposed network architecture. A description of the main building blocks of the network is also presented. The network is synthesized for various sizes and platforms, and the performance results are presented and analyzed. Our synthesized network achieves a 251 times speed up over a custom built software network, running on an i7 3770k Desktop computer, proving the benefits of parallel computation for this kind of network.
2016 International Conference on ReConFigurable Computing and FPGAs (ReConFig)