论文标题
雷克光：具有集成硅光子学的复发性神经网络加速器
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics
论文作者
论文摘要
复发性神经网络（RNN）用于在数据序列中学习依赖性的应用，例如语音识别，人类活动识别和异常检测。近年来，GRUS和LSTMS等较新的RNN变体已用于实施这些应用程序。由于许多这些应用都在实时场景中使用，因此加速RNN/LSTM/GRU推断至关重要。在本文中，我们提出了一种新型的光子硬件加速器，称为Reclight，用于加速简单的RNN，GRU和LSTMS。仿真结果表明，与最先进的情况相比，重新调整的每位能量低37倍，吞吐量要高10％。
Recurrent Neural Networks (RNNs) are used in applications that learn dependencies in data sequences, such as speech recognition, human activity recognition, and anomaly detection. In recent years, newer RNN variants, such as GRUs and LSTMs, have been used for implementing these applications. As many of these applications are employed in real-time scenarios, accelerating RNN/LSTM/GRU inference is crucial. In this paper, we propose a novel photonic hardware accelerator called RecLight for accelerating simple RNNs, GRUs, and LSTMs. Simulation results indicate that RecLight achieves 37x lower energy-per-bit and 10% better throughput compared to the state-of-the-art.
