high performance computing on graphics processing units: hgpu.org

JCRAM 2018 welcomes researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Robotics, Automation and Mechatronics can be shared and examined. The conference is an ideal platform for keeping up with advances and changes to a consistently morphing field. Publication and Indexing All accepted papers will be published in […]

JCRAI 2018 welcomes researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Robotics and Artificial Intelligence can be shared and examined. The conference is an ideal platform for keeping up with advances and changes to a consistently morphing field. Publication and Indexing All accepted papers will be published in […]

CCVPR 2018 welcomes researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Computer Vision and Pattern Recognition can be shared and examined. The conference is an ideal platform for keeping up with advances and changes to a consistently morphing field. Publication and Indexing All accepted papers will be published […]

Convolutional Neural Networks (CNNs) are extremely computationally demanding, presenting a large barrier to their deployment on resource-constrained devices. Since such systems are where some of their most useful applications lie (e.g. obstacle detection for mobile robots, vision-based medical assistive technology), significant bodies of work from both machine learning and systems communities have attempted to provide […]

Over the past few years, in the huge field of Artificial Intelligence (AI), new Machine Learning techniques are playing a central role, proving to be very powerful and versatile. For this reason, it is expected that they could become protagonist of space applications and they are already under study. Thanks to the large availability of […]

Medical applications challenge today’s text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead in accuracy, this leap comes at the sacrifice of interpretability. To address this accuracy-interpretability challenge, we here introduce, for the first time, a text categorization approach that leverages the recently introduced Tsetlin Machine. […]

Gradient boosted decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art performance in a wide variety of machine learning tasks. In this paper, we present an extensive empirical comparison of XGBoost, LightGBM and CatBoost, three popular GBDT algorithms, to aid the data science practitioner in the […]

In this work, we are proposing the ZUCL framework for implementing and running OpenCL applications for the latest Xilinx ZYNQ UltraScale+ platform. ZUCL is a holistic framework addressing the FPGA OS infrastructure, high level synthesis (HLS) module implementation as well as the runtime management. ZUCL enables partial reconfiguration (PR) on this platform by providing an […]

k-Means is a versatile clustering algorithm widely used in practice. To cluster large data sets, state-of-the-art implementations use GPUs to shorten the data to knowledge time. These implementations commonly assign points on a GPU and update centroids on a CPU. We identify two main shortcomings of this approach. First, it requires expensive data exchange between […]

Sparse matrix-vector multiplication (SpMV) can be used to solve diverse-scaled linear systems and eigenvalue problems that exist in numerous, and varying scientific applications. One of the scientific applications that SpMV is involved in is known as Configuration Interaction (CI). CI is a linear method for solving the non-relativistic Schroedinger equation for quantum chemical multi-electron systems, […]

Deep learning recently has been showing superior performance in complex domains such as computer vision, audio processing and natural language processing compared to traditional statistical methods. Naturally, deep learning techniques, combined with large electronic health records (EHR) data generated from healthcare organizations have potential to bring dramatic changes to the healthcare industry. However, typical deep […]

Accurately predicting key combustion phenomena in reactive-flow simulations, e.g., lean blow-out, extinction/ignition limits and pollutant formation, necessitates the use of detailed chemical kinetics. The large size and high levels of numerical stiffness typically present in chemical kinetic models relevant to transportation/power-generation applications make the efficient evaluation/factorization of the chemical kinetic Jacobian and thermochemical source-terms critical […]