AstraLaunch provides assessment, commercialization potential, market intelligence, and helps to prioritize research and technology portfolios. This is accomplished by merging an existing technology assessment program developed at the Muenster University of Applied Sciences in Germany with the Bintel Intelligence Platform. The prototype will first assess the technology based on 43 researched criteria, then use Bintel for collection, processing, analyzing and storing the source abstracts, news and information in an enriched SQL database stored on AWS. This system allows researchers, technology transfer, portfolio managers to quickly develop a comprehensive understanding on any given topic. This is done by collecting, processing, and aggregating market information related to that topic, and presenting it in intuitive visualizations. The system’s benefit keeping the researcher up-to-date in a fast changing and increasingly complex world. The system delivers user-curated updates continuously through the online platform, allowing the end-user to quickly spot changes in trends, new developments, or changes in strategy from competitors or incumbents. This provides a significant informational advantage to the end-user: they will have a superior top-down view of specific topics or groups of topics that interest them, allowing them to build more informed bottom-up analysis or integrated products.
Optimized Energy Efficient Prefetcher Hardware Architecture
With rapidly increasing parallelism, DRAM performance and power have surfaced as primary constraints from consumer electronics to high performance computing (HPC) for bulk-synchronous data-parallel applications that are key drivers for multi-core, e.g., image processing, climate modeling, physics simulation, gaming, face recognition, and many others. Lawrence Berkeley National Laboratory’s optimized energy efficient prefetcher hardware architecture, a purely hardware last-level cache prefetcher, exploits the highly correlated prefetch patterns of data-parallel algorithms not recognized by prefetchers oblivious to data parallelism. The technology generates prefetches on behalf of multiple cores in memory address order to maximize DRAM efficiency and bandwidth. It can prefetch from multiple memory pages without expensive translations. Compared to other prefetchers, the LLNL technology improves execution time by 5.5% on average (10% maximum), increases DRAM bandwidth by 9% to 18%, decreases DRAM rank energy by 6%, produces 27% more timely prefetches, and increases coverage by 25% at minimum.
HADES - High-Fidelity Adaptive Deception & Emulation System
The HADES platform is a deception environment that utilizes Software Defined Networks (SDN), cloud computing, dynamic deception, and agentless Virtual Machine Introspection (VMI). These elements fuse to not only create complex, high-fidelity deception networks, but also provide mechanisms to directly interact with the adversary—something current deception products do not facilitate. At the onset of an attack, adversaries are migrated into an emulated deception environment, where they are able to carry out their attacks without any indication that they have been detected or are being observed. HADES then allows the defender to react to adversarial attacks in a methodical and proactive manner by modifying the environment, host attributes, files, and the network itself in real-time. Through a rich set of data and analytics, cybersecurity practitioners gain valuable information about the tools and techniques used by their adversaries, which can then be fed back to the network defender as threat intelligence.