Virtual prototyping of floating point units

International audienceVirtual prototyping is a key technology for design space exploration, design verification, code development and testing of new systems on chip. For processors, either general purpose, multi and manycore or GPUs, the availability of powerful floating point units has now become mandatory in a lot of markets. However, implementing a fast and accurate floating point unit virtual prototype is not easy, even if host and target processors are IEEE 754 compliant. The goal of this paper is not to propose a definitive solution to this problem, but to draw the attention of the virtual prototyping community to it. To that aim, we study two solutions that form a compromise between speed of virtual prototype execution and computation accuracy

A discussion of privacy challenges in user profiling with big data techniques : the EEXCESS use case

User profiling is the process of collecting information about a user in order to construct their profile. The information in a user profile may include various attributes of a user such as geographical location, academic and professional background, membership in groups, interests, preferences, opinions, etc. Big data techniques enable collecting accurate and rich information for user profiles, in particular due to their ability to process unstructured as well as structured information in high volumes from multiple sources. Accurate and rich user profiles are important for applications such as recommender systems, which try to predict elements that a user has not yet considered but may find useful. The information contained in user profiles is personal and thus there are privacy issues related to user profiling. In this position paper, we discuss user profiling with big data techniques and the associated privacy challenges. We also discuss the ongoing EU-funded EEXCESS project as a concrete example of constructing user profiles with big data techniques and the approaches being considered for preserving user privacy