Cognitive Data-Centric Systems
Leland Chang · 2017
With rapid growth in the availability of massive amounts of data and the development of new machine learning and deep learning techniques, significant opportunities exist in the application of computing to learn from data, build models, and discover insights -- cognitive tasks that can augment human expertise in a broad range of industries. Computing systems must evolve to efficiently meet these needs by leveraging innovation in heterogeneous systems infrastructure and information technology consumption models that are increasingly driven by cloud-based delivery. These new systems must be designed to accommodate the entirety of the overall workflow, including not just machine learning and analytics tasks, but also data management and manipulation. In a convergence with systems for classical modeling and simulation (HPC and technical computing), cognitive workloads can benefit dramatically from hardware acceleration. As decades of sustained CMOS technology scaling begins to slow, the specificity and optimality of hardware accelerators will be a key enabler for system-level performance while simultaneously presenting challenges in composing systems that seamlessly integrate traditional CPUs, multiple accelerators, and different memories. This talk will discuss cognitive data-centric systems for the next era of computing, in which balanced heterogeneous systems are delivered through the cloud.