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.
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.