When Massive GPU Parallelism Ain't Enough
Vladimir Rybalkin, Norbert Wehn
Abstract
Multidimensional Long Short-Term Memory (MD-LSTM) neural network is an extension of one-dimensional LSTM for data with more than one dimension that allows MD-LSTM to show state-of-the-art results in various applications including handwritten text recognition, medical imaging, and many more. However, efficient implementation suffers from very sequential execution that tremendously slows down both training and inference compared to other neural networks. This is the primary reason that prevents intensive research involving MD-LSTM in the recent years, despite large progress in microelectronics and architectures. The main goal of the current research is to provide acceleration for inference of MD-LSTM, so to open a door for efficient training that can boost application of MD-LSTM. By this research we advocate that FPGA is an alternative platform for deep learning that can offer a solution in cases when a massive parallelism of GPUs does not provide the necessary performance required by the application. In this paper, we present the first hardware architecture for MD-LSTM. We conduct a systematic exploration of precision vs. accuracy trade-off using challenging dataset for historical document image binarization from DIBCO 2017 contest, and well known MNIST dataset for handwritten digits recognition. Based on our new architecture we implement FPGA-based accelerator that outperforms NVIDIA K80 GPU implementation in terms of runtime by up to 50x and energy efficiency by up to 746x. At the same time, our accelerator demonstrates higher accuracy and comparable throughput in comparison with state-of-the-art FPGA-based implementations of multilayer perceptron for MNIST dataset.