Sparse general matrix-matrix multiplication (spGEMM) is an essential
component in many scientific and data analytics applications. However, the
sparsity pattern of the input matrices and the interaction of their patterns
make spGEMM challenging. Modern GPUs include Tensor Core Units (TCUs), which
specialize in dense matrix multiplication. Our aim is to re-purpose TCUs for
sparse matrices. The key idea of our spGEMM algorithm, tSparse, is to multiply
sparse rectangular blocks using the mixed precision mode of TCUs. tSparse
partitions the input matrices into tiles and operates only on tiles which
contain one or more elements. It creates a task list of the tiles, and performs
matrix multiplication of these tiles using TCUs. To the best of our knowledge,
this is the first time that TCUs are used in the context of spGEMM. We show
that spGEMM, with our tiling approach, benefits from TCUs. Our approach
significantly improves the performance of spGEMM in comparison to cuSPARSE,
CUSP, RMerge2, Nsparse, AC-SpGEMM and spECK.

通过将稀疏矩形块进行分割，利用 Tensor Core Units（TCUs）的混合精度模式实现的稀疏矩阵乘法算法 tSparse，可以显著比常用的 cuSPARSE、CUSP、RMerge2、Nsparse、AC-SpGEMM 和 spECK 的方法提高性能。

利用 GPU 张量核加速稀疏矩阵 - 矩阵乘法

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

An accurate and reliable image based fruit detection system is critical for
supporting higher level agriculture tasks such as yield mapping and robotic
harvesting. This paper presents the use of a state-of-the-art object detection
framework, Faster R-CNN, in the context of fruit detection in orchards,
including mangoes, almonds and apples. Ablation studies are presented to better
understand the practical deployment of the detection network, including how
much training data is required to capture variability in the dataset. Data
augmentation techniques are shown to yield significant performance gains,
resulting in a greater than two-fold reduction in the number of training images
required. In contrast, transferring knowledge between orchards contributed to
negligible performance gain over initialising the Deep Convolutional Neural
Network directly from ImageNet features. Finally, to operate over orchard data
containing between 100-1000 fruit per image, a tiling approach is introduced
for the Faster R-CNN framework. The study has resulted in the best yet
detection performance for these orchards relative to previous works, with an
F1-score of >0.9 achieved for apples and mangoes.

本文介绍了使用先进的物体检测框架 Faster R-CNN 对果园中的水果进行检测，包括芒果、杏仁和苹果，通过消融实验，数据增强技术和瓦片方法来提高检测精度和效率，并获得比以前更好的检测表现，对于苹果和芒果的 F1 值达到了 > 0.9。