Driving critical research in computer science through academic collaboration.
Centered at Carnegie Mellon University, the ISTC for Visual Cloud Systems aims to record and anaylze the world's visual information so that computers (not humans) can understand and reason about it.
Centered at Georgia Tech, the ISTC for Adversarial-Resilient Security Analytics will study the vulnerabilities of ML algorithms and develop new security approaches to improve the resilience of ML applications.
Colocated between UC Berkeley and Stanford University, the Agile Design center aims to enable a more agile hardware development flow, to quickly and easily modify an existing design.
Based in China at Tsinghua University, the ICRI for Mobile Networking and Computing is exploring advanced mobile network technologies to support typical applications in the next generation (5G) networks.
Based in Taiwan at the National Taiwan University, this ICRI will serve as a conduit for research collaboration with the global and local industries to develop practical products and services.
Based in Europe, this ICRI will address the major open problems in the design and deployment of intelligent systems that function in the physical world.
Based in Germany, this ICRI will investigate new opportunities for developing significant improvement to (a) the security of autonomous platforms as well as (b) self-defense capabilities of distributed systems.
This program with researchers from Berkeley, Stanford, and CMU is focused on advancing state-of-the-art in deep learning while optimizing it for IA platforms.
Developing techniques to enable energy-smart solutions for system-on-a-chip (SOC) device rapid prototyping.
Making FPGAs more accessible to software developers and expanding the applicability of FPGAs across the compute continuum, from wearables to data centers and supercomputers.
Developing mobile computing SoC architecture for eight-hour sustained operation and to improve efficiency of power/performance by 10x for graphics, media, and sensor IP.
Aims to extract key insights from neuroscience at the algorithmic level to provide guidance on future directions for neuromorphic computing architectures.
Researching approaches to efficiently enable tunability of front end module (FEM) passive filters in mobile RF transceivers.
Looks at approaches for enabling a new generation of ultra-low power (ULP) radios for active low-cost wireless sensor and compute platforms.
The Center for Domain-Specific Computing (CDSC) is researching accelerator-rich architectures with applications to health care, in which personalized cancer treatment is added as an application domain in addition to medical imaging.
Recognizes the shift from transistor-scaling-driven performance improvements to a new post-scaling world where whole-stack co-design is the key to improved efficiency.
Brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, and robotics. BAIR includes over two dozen faculty and more than a hundred graduate students pursuing research on fundamental advances in the above areas as well as cross-cutting themes including multi-modal deep learning, human-compatible AI, and connecting AI with other scientific disciplines and the humanities.
Stanford based research project launched in 2017 to design systems and tools for usable machine learning, enabling non-experts to build and run production-ready ML apps.
A 5-year research project focused on solving the systems, machine learning, and security challenges required to create an oepn-source, general-purpose, secure stack that can make intelligent decisions on live data in real-time.
The Stanford Data Science Initiative (SDSI) is a university-wide organization focused on core data technologies with strong ties to application areas across campus. SDSI comprises methods research, infrastructure, and education.
Foster novel, multidisciplinary approaches that will transform the way people interact with visual information through mobile and wearable devices, autonomous machines, and semantically aware spaces.
Transform the way people interact with engineered systems and address threats stemming from increasing reliance on computer and communication technologies.
Seeks unique data network architectures featuring an information plane using an Information-Centric Networking (ICN) approach and addressing discovery, movement, delivery, management, and protection of information within a network, along with the abstraction of an underlying communication plane creating opportunities for new efficiencies and optimizations across communications technologies that could also address latency and scale requirements.
Addresses the problem of effective software development for diverse hardware architectures through groundbreaking university research that will lead to a significant, measurable leap in software development productivity by partially or fully automating software development tasks that are currently performed by humans.
ASCENT focuses on demonstration of foundational material synthesis routes and device technologies, novel heterogeneous integration (package and monolithic) schemes to support the next era of “functional hyper-scaling.”
The ADA Center will reignite system design innovation by drawing on opportunities in application driven architecture and system-driven technology advances, with support from agile system design frameworks that encompass programming languages to implementation technologies.
The mission of CBRIC is to deliver key advances in cognitive computing, with the goal of enabling a new generation of autonomous intelligent systems.
ComSenTer will develop the technologies for a future cellular infrastructure using hubs with massive spatial multiplexing, providing 1-100Gb/s to the end user, and, with 100-1000 simultaneous independently-modulated beams, aggregate hubs capacities in the 10s of Tb/s.
CONIX aims to provide a new middle tier of distributed computing that tightly couples the cloud and edge by pushing increased levels of autonomy and intelligence into the network.
The CRISP grand challenge is to significantly lower the effort barrier for every day programmers to achieve highly portable, “bare-metal,” and understandable performance across a wide range of heterogeneous, IMS architectures.
The nanoelectronic COmputing REsearch (nCORE) program funds collaborative university research in the U.S. to develop key technologies to enable novel computing and storage paradigms with long-term impact on the semiconductor, electronics, computing, and defense industries. The nCORE program supports the National Strategic Computing Initiative (NSCI) through government-industry-academia collaborations. It will be driven by fundamental research on emerging materials and devices with the potential to achieve significantly improved efficiency, enhanced performance, and new functionalities, beyond the capability of conventional CMOS technologies. The new program is built upon the learning from the Nanoelectronics Research Initiative (NRI).
This center aimed to create demonstrable machine-to-machine (M2M) technologies that can showcase the potential of these technologies to transform our everyday activities and environment. Located in Taiwain—NTU. 2010-2016
Hubbed in Germany at Saarland University, the Institute focused on Visual Computing research, meaning the acquisition, modeling, processing, transmission, rendering, and display of visual and associated data. 2012-2016
Based in England at University College London, Imperial College London, and Future Cities Catapult this center researches the compute fabric needed to support an urban Internet of Things at city scale. 2012-2017
Based in Germany at TU Darmstadt, this center explores lightweight, cost-effective security and trust anchor primitives for IoT edge devices with integrated outputs into flexible and agile silicon prototypes. 2013-2017
Based in Israel at Technion and Hebrew University, this center focuses on hardware/software innovations for accelerating Machine Learning and Cognitive Applications. 2013-2017
Developing novel algorithms, architectures, accelerators, circuits, and power management techniques that optimally exploit randomized compressive measurements and compressed domain data processing for 2D/3D still/video/MRI images. 2014-2017
Develop techniques for effectively summarizing the video egocentric cameras collect and develop solutions for extracting the useful information embedded in the raw data (first-person video, images, audio, and location) egocentric cameras collect and presenting this information to the user on demand. 2014-2017
Develop innovative techniques to reducing memory latency, create low latency storage systems, and accelerate progress in general-purpose microarchitectures, and accelerator architectures. 2014-2017
Seeks to make networks more amenable to innovation by extending the benefits of SDN to carrier networks. Research vectors include SDN for carriers, processing traffic in software, services architecture, and deployment scenarios. 2014-2017
Centered at Stanford University. The center sought to bring modern trends in computing (the cloud, crowd sourcing, hand-held computing) to bear on hybrids of computer graphics, animation, image understanding, and large-scale gaming. 2011-2015
Centered at CMU. Smart Headlights and AndyVision—a planogram generating robot—were both featured at Research at Intel Day in 2012 and were just a few of the many success stories highlighted during the life of the center. 2011-2014
Centered at Carnegie Mellon University, the ISTC for Cloud Computing research community researched critical new underlying technologies for cloud computing of the future. 2011-2016
Centered at the University of Washington, the ISTC for Pervasive Computing brought together research leaders in wireless communication and sensing, AI and ML, computer vision, HCI, and security. 2011-2016
Centered at the University of California, Berkeley, the ISTC Center for Secure Computing focuses on scientific research to make computing technology safe and secure for users. 2011-2016
Centered at MIT, the ISTC for Big Data is exploring data analytics to support data-intensive discovery including database management, analytics and visualization support. 2012-2017