Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications
| Autor: | Daehyun Kim, Christopher J. Hughes, Mikhail Smelyanskiy, Yen-Kuang Chen, Anthony-Trung D. Nguyen, Pradeep Dubey, Victor W. Lee, Jatin Chhugani, Sanjeev Kumar |
|---|---|
| Rok vydání: | 2008 |
| Předmět: | |
| Zdroj: | Proceedings of the IEEE. 96:790-807 |
| ISSN: | 1558-2256 0018-9219 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|