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Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality. The HPC AI benchmarks accelerate the process. Unfortunately, benchmarking HPC AI systems at scale raises serious challenges. This paper presents a comprehensive HPC AI benchmarking methodology that achieves equivalence, representativeness, repeatability, and affordability. Among the nineteen AI workloads of AIBench Training–by far the most comprehensive AI benchmarks suite, we choose two representative and repeatable AI workloads in terms of both AI model and micro-architectural characteristics. The selected HPC AI benchmarks include both business and scientific computing: Image Classification and Extreme Weather Analytics. Finally, we propose three high levels of benchmarking and the corresponding rules to assure equivalence. To rank the performance of HPC AI systems, we present a new metric named Valid FLOPS, emphasizing both throughput performance and target quality. The evaluations show our methodology, benchmarks, and metrics can measure and rank the HPC AI systems in a simple, affordable and repeatable way. The specification, source code, datasets, and HPC AI500 ranking numbers are publicly available from https://www.benchcouncil.org/aibench/hpcai500/index.html.

Face presentation attack detection (PAD) has drawn increasing attentions to secure face recognition (FR) systems which are being widely used in many applications from access control to smartphone unlock. Traditional approaches for PAD may lack good generalization capability into new application scenarios due to the limited number of subjects and data modality. In this work, we propose an end-to-end multi-modal fusion approach via spatial and channel attention to improve PAD performance on CASIA-SURF. Specifically, We first build four branches integrated with spatial and channel attention module to obtain the uniform features of different modalities, i.e., RGB, Depth, IR and the fused modality with 9 channels which concatenating three modalities. Subsequently, the features extracted from the four branches are concatenated and fed into the shared layers to learn more discriminative features from the fusion perspective. Finally, we get the classification confidence scores w.r.t. PAD or not. The entire network is optimized with the joint of the center loss and softmax loss and SGRD solver to update the parameters. The proposed approach shows promising results on the CASIA-SURF dataset.