Yongming Shen

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Convolutional neural networks (CNNs) are revolutionizing machine learning, but they present significant computational challenges. Recently, many FPGA-based accelerators have been proposed to improve the performance and efficiency of CNNs. Current approaches construct a single processor that computes the CNN layers one at a time; the processor is optimized(More)
A parallel, unstructured grid, finite-volume parametric wind-wave model is developed with the intention of coupling to three-dimensional unstructured grid ocean circulation models. The model is derived from a conservation of energy flux formulations. In the model, the shoaling, refraction, and wave dissipation, as well as exchange of current and Stokes(More)
All software in use today relies on libraries, including standard libraries (e.g., C, C++) and application-specific libraries (e.g., libxml, libpng). Most libraries are loaded in memory and dynamically linked when programs are launched, resolving symbol addresses across the applications and libraries. Dynamic linking has many benefits: It allows code to be(More)
Convolutional neural networks (CNNs) are revolutionizing a variety of machine learning tasks, but they present significant computational challenges. Recently, FPGA-based accelerators have been proposed to improve the speed and efficiency of CNNs. Current approaches construct an accelerator optimized to maximize the overall throughput of iteratively(More)
Unprecedented open ended nitrogen doped carbon nanotubes prepared by direct pyrolysis of [Co(HTTG)(H(2)O)(2)](n) (TTG = N,N',N''-1,3,5-triazine-2,4,6-triyltris-glycine) show among the largest surface area of multiwalled nanotubes and high CO(2)/CH(4) adsorption selectivity which increases with increasing of nitrogen content.
Convolutional neural networks (CNNs) are used to solve many challenging machine learning problems. Interest in CNNs has led to the design of CNN accelerators to improve CNN evaluation throughput and efficiency. Importantly, the bandwidth demand from weight data transfer for modern large CNNs causes CNN accelerators to be severely bandwidth bottlenecked,(More)
The popularity of online services has grown exponentially, spurring great interest in improving server hardware and software. However, conducting research on servers has traditionally been challenging due to the complexity of setting up representative server configurations and measuring their performance. Recent work has eased the effort of benchmarking(More)
Convolutional neural networks (CNNs) are used to solve many challenging machine learning problems. These networks typically use convolutional layers for feature extraction and fully-connected layers to perform classification using those features. Significant interest in improving the performance of CNNs has led to the design of CNN accelerators to improve(More)
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