SENSE: Sketching Framework for Big Data Acceleration on Low Power Embedded Cores
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-02807-7_10
Reference25 articles.
1. Kulkarni, A., Shea, C., Homayoun, H., Mohsenin, T.: LESS: big data sketching and encryption on low power platform. In: 2017 Design, Automation Test in Europe Conference Exhibition (DATE) (2017)
2. Kulkarni, A., et al.: Low overhead CS-based heterogeneous framework for big data acceleration. In: Proceedings of the ACM Transactions Embedded Computing System 17, 1, Article 25 (2017), 25 pages, GLSVLSI ’16, pp. 57–62. ACM (2017)
3. Kulkarni, A., et al.: CS-based secured big data processing on FPGA. In: 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp. 201–201 (2016)
4. Rouhani, B., et al.: Sketch: An automated framework for streaming sketch-based analysis of big data on fpga. In: 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp. 187–194 (2015)
5. Mirhoseini, A., et al.: Perform-ml: performance optimized machine learning by platform and content aware customization. In: Proceedings of the 53rd Annual Design Automation Conference, DAC ’16, pp. 20:1–20:6. ACM, New York, NY, USA(2016)
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