• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on computer design >An FPGA Implementation of Stochastic Computing-Based LSTM
【24h】

An FPGA Implementation of Stochastic Computing-Based LSTM

机译：基于随机计算的LSTM的FPGA实现

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As a special type of recurrent neural networks (RNN), Long Short Term Memory (LSTM) is capable of processing sequential data with a great improvement in accuracy and is widely applied in image/video recognition and speech recognition. However, LSTM typically possesses high computational complexity and may cause high hardware cost and power consumption when being implemented. With the development of Internet of Things (IoT) and mobile/edge computation, lots of mobile and edge devices with limited resources are widely deployed, which further exacerbates the situation. Recently, Stochastic Computing (SC) has been applied in to neural networks (NN) (e.g., convolution neural networks, CNN) structure to improve power efficiency. Essentially, SC can effectively simplify the fundamental arithmetic circuits (e.g., multiplication), and reduce the hardware cost and power consumption. Therefore, this paper introduces SC into LSTM and creatively proposes an SC-based LSTM architecture design to save the hardware cost and power consumption. More importantly, the paper successfully implements the design on a Field Programmable Gate Array (FPGA) and evaluates its performance on the MNIST dataset. The evaluation results show that the SC-LSTM design works smoothly and can significantly reduce power consumption by 73.24% compared to the baseline binary LSTM implementation without much accuracy loss. In the future, SC can potentially save hardware cost and reduce power consumption in a wide range of IoT and mobile/edge applications.
机译：作为一种特殊类型的递归神经网络（RNN），长短期记忆（LSTM）能够处理顺序数据，并且准确性大大提高，并广泛应用于图像/视频识别和语音识别。但是，LSTM通常具有较高的计算复杂度，并且在实施时可能会导致较高的硬件成本和功耗。随着物联网（IoT）和移动/边缘计算的发展，大量有限资源的移动和边缘设备被广泛部署，这进一步加剧了这种情况。最近，随机计算（SC）已经被应用于神经网络（NN）（例如，卷积神经网络，CNN）的结构以提高功率效率。本质上，SC可以有效地简化基本算术电路（例如乘法），并降低硬件成本和功耗。因此，本文将SC引入LSTM，并创造性地提出了一种基于SC的LSTM架构设计，以节省硬件成本和功耗。更重要的是，本文成功地在现场可编程门阵列（FPGA）上实现了该设计，并在MNIST数据集上评估了其性能。评估结果表明，与基准二进制LSTM实施相比，SC-LSTM设计可以平稳运行，并且可以将功耗大幅降低73.24％，而不会造成太大的精度损失。将来，SC可以潜在地节省硬件成本并降低各种IoT和移动/边缘应用程序的功耗。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号