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» A learning machine for resource-limited adaptive hardware
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AHS
2007
IEEE
219views Hardware» more  AHS 2007»
13 years 11 months ago
A learning machine for resource-limited adaptive hardware
Machine Learning algorithms allow to create highly adaptable systems, since their functionality only depends on the features of the inputs and the coefficients found during the tr...
Davide Anguita, Alessandro Ghio, Stefano Pischiutt...
IJPP
2011
115views more  IJPP 2011»
12 years 8 months ago
Milepost GCC: Machine Learning Enabled Self-tuning Compiler
Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization i...
Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon...
ICCAD
2006
IEEE
119views Hardware» more  ICCAD 2006»
13 years 10 months ago
Dynamic power management using machine learning
Dynamic power management (DPM) work proposed to date places inactive components into low power states using a single DPM policy. In contrast, we instead dynamically select among a...
Gaurav Dhiman, Tajana Simunic Rosing
ICDE
2008
IEEE
190views Database» more  ICDE 2008»
14 years 6 months ago
Adaptive Segmentation for Scientific Databases
In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentati...
Milena Ivanova, Martin L. Kersten, Niels Nes
MICRO
2008
IEEE
148views Hardware» more  MICRO 2008»
13 years 11 months ago
Coordinated management of multiple interacting resources in chip multiprocessors: A machine learning approach
—Efficient sharing of system resources is critical to obtaining high utilization and enforcing system-level performance objectives on chip multiprocessors (CMPs). Although sever...
Ramazan Bitirgen, Engin Ipek, José F. Mart&...