• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs
【24h】

Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs

机译：通过TITE GPU的比特 - 张量芯加速二值化神经网络

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1 percent) when executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs start to experimentally support bit computation. In this article, we look into this brand new bit computation capability and characterize its unique features. We show that the stride of memory access can significantly affect performance delivery and a data-format co-design is highly desired to support the tensorcores for achieving superior performance than existing software solutions without tensorcores. We realize the tensorcore-accelerated BNN design, particularly the major functions for fully-connect and convolution layers - bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a rate of 5.6K images per second, 77 percent faster than state-of-the-art. Our BNN approach is released on https://github.com/pnnl/TCBNN.
机译：尽管对传统的深度神经网络进行了巨大的加速，但二值化神经网络（BNN）的性能优势仅在CPU和GPU等通用处理器上展示了展示。事实上，由于无法利用与基于词的架构的比特级并行性，在执行BNN时，GPU被批评为极低的利用率（1％）。因此，NVIDIA中的最新Tensorcores Ty Ty GPU开始通过实验支持位计算。在本文中，我们研究了这个全新的比特计算能力，并表征了其独特的功能。我们表明，存储器访问的步幅可以显着影响性能传递，并且非常需要数据格式共同设计来支持比现有的软件解决方案实现卓越的性能而没有Tensorcores。我们实现了Tensorcore加速的BNN设计，特别是全连接和卷积层的主要功能 - 比特矩阵乘法和比特卷积。对两种NVIDIA进行GPU的评估表明，随着Reset-18，我们的BTC-BNN设计可以以每秒5.6k图像的速度处理想象成，比最先进的77％更快。我们的BNN方法是在https://github.com/pnnl/tcbnn上发布的。

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