LSTM-Sharp: An Adaptable, Energy-Efficient Hardware Accelerator for Long Short-Term Memory

The effectiveness of LSTM neural networks for popular tasks such as Automatic Speech Recognition has fostered an increasing interest in LSTM inference acceleration. Due to the recurrent nature and data dependencies of LSTM computations, designing a customized architecture specifically tailored to its computation pattern is crucial for efficiency. Since LSTMs are used for a variety of tasks, generalizing this efficiency to diverse configurations, i.e., adaptiveness, is another key feature of these accelerators. In this work, we first show the problem of low resource-utilization and adaptiveness for the state-of-the-art LSTM implementations on GPU, FPGA and ASIC architectures. To solve these issues, we propose an intelligent tiled-based dispatching mechanism that efficiently handles the data dependencies and increases the adaptiveness of LSTM computation. To do so, we propose LSTM-Sharp as a hardware accelerator, which pipelines LSTM computation using an effective scheduling scheme to hide most of the dependent serialization. Furthermore, LSTM-Sharp employs dynamic reconfigurable architecture to adapt to the model's characteristics. LSTM-Sharp achieves 1.5x, 2.86x, and 82x speedups on average over the state-of-the-art ASIC, FPGA, and GPU implementations respectively, for different LSTM models and resource budgets. Furthermore, we provide significant energy-reduction with respect to the previous solutions, due to the low power dissipation of LSTM-Sharp (383 GFLOPs/Watt).