Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications
- 15 April 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Proceedings of the IEEE
- Vol. 96 (5), 790-807
- https://doi.org/10.1109/jproc.2008.917729
Abstract
This paper examines the growing need for a general-purpose ldquoanalytics enginerdquo that can enable next-generation processing platforms to effectively model events, objects, and concepts based on end-user input, and accessible datasets, along with an ability to iteratively refine the model in real-time. We find such processing needs at the heart of many emerging applications and services. This processing is further decomposed in terms of an integration of three fundamental compute capabilities-recognition, mining, and synthesis (RMS). The set of RMS workloads is examined next in terms of usage, mathematical models, numerical algorithms, and underlying data structures. Our analysis suggests a workload convergence that is analyzed next for its platform implications. In summary, a diverse set of emerging RMS applications from market segments like graphics, gaming, media-mining, unstructured information management, financial analytics, and interactive virtual communities presents a relatively focused, highly overlapping set of common platform challenges. A general-purpose processing platform designed to address these challenges has the potential for significantly enhancing users' experience and programmer productivity.Keywords
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