Real-time data is a sample covering the last seven days. There are two samples of Google Trends data that can be accessed: This allows us to display interest in a particular topic from around the globe or down to city-level geography. It’s anonymized (no one is personally identified), categorized (determining the topic for a search query) and aggregated (grouped together). 19–24.Google Trends provides access to a largely unfiltered sample of actual search requests made to Google. In 28th IEEE International Conference on Application-specific Systems, Architectures and Processors, ASAP 2017, Seattle, WA, USA, July 10-12, 2017. Parallel Multi Channel convolution using General Matrix Multiplication. Aravind Vasudevan, Andrew Anderson, and David Gregg.In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’16). Latte: A Language, Compiler, and Runtime for Elegant and Efficient Deep Neural Networks. Leonard Truong, Rajkishore Barik, Ehsan Totoni, Hai Liu, Chick Markley, Armando Fox, and Tatiana Shpeisman.In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E.Very Deep Convolutional Networks for Large-Scale Image Recognition.
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EECS Department, University of California, Berkeley. Learning Semantic Image Representations at a Large Scale. Springer Berlin Heidelberg, Berlin, Heidelberg, 346–361. Nearly Optimal Register Allocation with PBQP. Springer Berlin Heidelberg, Berlin, Heidelberg, 49–65.
Code Instruction Selection Based on SSA-Graphs. Erik Eckstein, Oliver König, and Bernhard Scholz.Presentation at Autodiff Workshop, co-located with NIPS 2016. Cambridge University Press, Cambridge, UK. Low-memory GEMM-based convolution algorithms for deep neural networks. Andrew Anderson, Aravind Vasudevan, Cormac Keane, and David Gregg.We show experimentally that significant gains are possible versus the state of the art vendor libraries by using a principled analytic solution to the problem of primitive selection in the presence of data layout transformations. We propose an analytic solution via a PBQP solver, and evaluate our approach experimentally by optimizing several popular DNNs using a library of more than 70 DNN primitives, on an embedded platform and a general purpose platform. We state the problem of optimal primitive selection in the presence of data layout transformations, and show that it is NP-hard by demonstrating an embedding in the Partitioned Boolean Quadratic Assignment problem (PBQP). In addition, specific algorithms operate much more efficiently on specialized data layouts. Deep Neural Networks (DNNs) require very large amounts of computation, and many different algorithms have been proposed to implement their most expensive layers, each of which has a large number of variants with different trade-offs of parallelism, locality, memory footprint, and execution time.