FPGA-based accelerator for long short-term memory recurrent neural networks
Yijin Guan, Zhihang Yuan, Guangyu Sun, Jason Cong · 2017
Long Short-Term Memory Recurrent neural networks (LSTM-RNNs) have been widely used for speech recognition, machine translation, scene analysis, etc. Unfortunately, general-purpose processors like CPUs and GPGPUs can not implement LSTM-RNNs efficiently due to the recurrent nature of LSTM-RNNs. FPGA-based accelerators have attracted attention of researchers because of good performance, high energy-efficiency and great flexibility. In this work, we present an FPGA-based accelerator for LSTM-RNNs that optimizes both computation performance and communication requirements. The peak performance of our accelerator achieves 7.26 GFLOP/S, which significantly outperforms previous approaches.