Today's auto-tuners (e. g., AutoTVM, Ansor) generate efficient tensor programs by navigating a large search space to identify effective implementations, but they do so with opaque hardware details.
Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values.
The quality metric proposed in this paper can significantly improve the performance of the recognition algorithm while reducing the number of images discarded for recognition, which is advantageous over hand-crafted factors based iris quality assessment methods.
Extensive experiments demonstrate the advantages of the PAAS technique to counter diverse face spoofing attacks (print, replay, mask) in uncontrolled indoor and outdoor conditions by learning polarized face images of 33 people.
In this paper, we propose a GPU-efficient subgraph isomorphism algorithm using the Gunrock graph analytic framework, GSM (Gunrock Subgraph Matching), to compute graph matching on GPUs.
Distributed, Parallel, and Cluster Computing
1 code implementation • 12 Feb 2018 • Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.