OpenHD: A GPU-Powered Framework for Hyperdimensional Computing
OpenHD: A GPU-Powered Framework for Hyperdimensional Computing
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DOI：
10.1109/tc.2022.3179226
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发表时间：
2022-11-01
影响因子：
3.7
通讯作者：
Kim, Yeseong
中科院分区：
文献类型：
--
作者：
Kang, Jaeyoung;Khaleghi, Behnam;Kim, Yeseong
超维计算（HDC）已经成为深度神经网络的替代轻量级学习解决方案。HDC的一个关键特性是可以促进硬件加速的高度并行性。然而，以前的HDC硬件实现很少关注GPU设计，这也是效率低下的部分原因是在GPU上加速HDC的复杂性。在本文中，我们提出了OpenHD，一个灵活的和高性能的GPU驱动的框架，用于自动化的一般HDC应用程序，包括分类和集群到GPU的映射。OpenHD利用专门针对HDC的内存优化策略，最大限度地减少对不同内存子系统的访问时间，并删除冗余操作。我们还提出了一种新的训练方法，使数据并行HDC训练。我们的评估结果表明，所提出的训练快速达到目标精度，将所需的训练时间减少了4倍。使用OpenHD，用户可以部署GPU加速的HDC应用程序，而无需领域专家知识。与最先进的GPU驱动的HDC实现相比，我们对NVIDIA Jetson TX2的评估显示，OpenHD在基于HDC的分类和聚类方面分别快了10.5倍和314倍。与GPU上的非HDC分类和聚类相比，基于OpenHD的HDC在相当的准确性下速度分别为11.7倍和53倍。OpenHD可在https://github.com/UCSD-SEELab/openhd上获得。
Hyperdimensional computing (HDC) has emerged as an alternative lightweight learning solution to deep neural networks. A key characteristic of HDC is the great extent of parallelism that can facilitate hardware acceleration. However, previous hardware implementations of HDC seldom focus on GPU designs, which were also inefficient partly due to the complexity of accelerating HDC on GPUs. In this paper, we present OpenHD, a flexible and high-performance GPU-powered framework for automating the mapping of general HDC applications including classification and clustering to GPUs. OpenHD takes advantage of memory optimization strategies specialized for HDC, minimizing the access time to different memory subsystems, and removing redundant operations. We also propose a novel training method to enable data parallelism in HDC training. Our evaluation result shows that the proposed training rapidly achieves the target accuracy, reducing the required training epochs by 4x. With OpenHD, users can deploy GPU-accelerated HDC applications without domain expert knowledge. Compared to the state-of-the-art GPU-powered HDC implementation, our evaluation on NVIDIA Jetson TX2 shows that OpenHD is up to 10.5x and 314x faster for HDC-based classification and clustering, respectively. Compared with non-HDC classification and clustering on GPUs, OpenHD-based HDC is 11.7x and 53x faster at comparable accuracy. OpenHD is available at: https://github.com/UCSD-SEELab/openhd.