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Compressive Sensing on Storage Data: An Effective Solution to Alleviate I/0 Bottleneck in Data- Intensive Workloads.

, , , , and . ASAP, page 1-8. IEEE Computer Society, (2018)

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Architectural considerations for FPGA acceleration of machine learning applications in MapReduce., , , , , and . SAMOS, page 89-96. ACM, (2018)Mitigating the Performance and Quality of Parallelized Compressive Sensing Reconstruction Using Image Stitching., , , , , , and . ACM Great Lakes Symposium on VLSI, page 219-224. ACM, (2019)Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design., , , , , , , , and . CoRR, (2019)Lightweight Node-level Malware Detection and Network-level Malware Confinement in IoT Networks., , , , , and . DATE, page 776-781. IEEE, (2019)XPPE: cross-platform performance estimation of hardware accelerators using machine learning., , , , , and . ASP-DAC, page 727-732. ACM, (2019)Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design., , , , , , and . FPL, page 397-403. IEEE, (2019)A comprehensive memory analysis of data intensive workloads on server class architecture., , , , and . MEMSYS, page 19-30. ACM, (2018)Main-Memory Requirements of Big Data Applications on Commodity Server Platform., , , and . CCGrid, page 653-660. IEEE Computer Society, (2018)Customized Machine Learning-Based Hardware-Assisted Malware Detection in Embedded Devices., , , , , and . TrustCom/BigDataSE, page 1685-1688. IEEE, (2018)Storage and Memory Characterization of Data Intensive Workloads for Bare Metal Cloud.. CoRR, (2018)