The resurgence of deep neural networks has led to a great performance boost in many application fields, especially for image recognition and video content analysis. The intensive computation and memory access pattern demands innovative heterogeneous computing, or to put it another way, dedicated coprocessors to accelerate the neural network training or inference. Ming Yang offers a brief introduction to deep neural network computation as well as an overview and comparison of the competing heterogeneous computing options, such as DSP, GPU, TPU, FPGA, and ASIC.
Ming Yang is the cofounder and vice president of software at Horizon Robotics. Previously, he was one of the founding members of the Facebook Artificial Intelligence Research (FAIR) team and a former senior researcher at NEC Labs America. Ming is a well-recognized researcher in computer vision and machine learning. His research interests include object tracking, face recognition, massive image retrieval, and multimedia content analysis. He holds 14 US patents and has over 20 publications in top conferences like CVPR and ICCV and 8 publications in top international journal T-PAMI, with more than 5,000 citations. During his tenure at Facebook, Ming led the deep learning research project Deep Face, which had a significant impact in the deep learning research community and was widely reported by media including Science magazine, MIT Tech Review, and Forbes. He has served as a member of the program committee for multiple top international conferences, including CVPR, ICCV, NIPS, and ACMMM. He has also been a reviewer for several top international journals, including T-PAMI, IJCV, and T-IP. As the leader of the NEC-UIUC team, Ming and his team achieved the best result in the TRECVid 2008 and 2009 Event Detection Evaluation. He was also a member of the NEC team that won first place in the ImageNet 2010 Large Scale Visual Recognition Challenge. He holds a BEng and MEng from the Department of Electrical Engineering at Tsinghua University and a PhD from the Department of Electrical Engineering and Computer Science at Northwestern University.
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