Research is Expanding the Future
Driving critical research in computer science through academic collaboration.
Driving critical research in computer science through academic collaboration.
Centered at MIT, the ISTC for Big Data is exploring data analytics to support data-intensive discovery including database management, analytics, and visualization support.
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 are more agile hardware development flow, to quickly and easily modify and existing design.
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.
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.
Based in Israel at Technion and Hebrew University, this center focuses on hardware/software innovations for accelerating machine learning and cognitive applications.
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.
Hubbed in Germany at Saarland University, the Institute focuses on visual computing research, meaning the acquisition, modeling, processing, transmission, rendering, and display of visual and associated data.
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.
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.
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.
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 improve efficiency of power/performance by 10x for graphics, media, and sensor IP.
Develop innovative techniques to reducing memory latency, create low latency storage systems, and accelerate progress in general-purpose microarchitectures and accelerator architectures.
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.
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.
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.
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 wearble 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.
Brings together experts in magnetic materials, spin transport, novel spin-transport materials, spintronic devices, circuits, and novel architectures to overcome constraints of conventional CMOS-based devices.
Focuses on successful innovation of application-driven architectures leveraging emerging circuit fabrics such as 3D interconnect, novel memories, and programmable logic for the transitional 2020s.
Designs and creates non-conventional materials and nanostructures with unique correlated quantum properties to accelerate innovation in analog, logic, and memory devices for semiconductor and defense applications.
Investigates the physics of new materials and devices toward disruptive advances in integrated circuits and systems. In particular, the prosing of the TFET transistor, a leading option for a post-CMOS switch.
Addresses challenges of achieving energy efficiency and robustness in the nanoscale era associated with the design of intelligent embedded systems on nanoscale CMOS and beyond CMOS device fabrics.
Aims to enable simple, reliable, and secure deployment of advanced distributed sense and control applications on massively distributed, and mostly uncoordinated, platforms through an open and universal systems architecture.
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.
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.
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.
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