CrystalBall: Statically analyzing runtime behavior via deep sequence learning

Article Details
Title:	CrystalBall: Statically analyzing runtime behavior via deep sequence learning
Article URLs:	https://doi.org/10.1109/MICRO.2016.7783727 http://doi.ieeecomputersociety.org/10.1109/MICRO.2016.7783727 http://dl.acm.org/citation.cfm?id=3195667
Alternative Article URLs:
Authors:	Stephen Zekany	Clinc, Inc.
	Daniel Rings	University of Michigan - Ann Arbor
	Nathan Harada	University of Michigan - Ann Arbor
	Michael A. Laurenzano	University of Michigan - Ann Arbor Clinc, Inc.
	Lingjia Tang	University of Michigan - Ann Arbor Clinc, Inc.
	Jason Mars	University of Michigan - Ann Arbor Clinc, Inc.
Sharing:	Unknown
Verification:	Authors have not verified information
Artifact Evaluation Badge:	none
Artifact URLs:	https://github.com/szekany/crystalball
Artifact Correspondence Email Addresses:
NSF Award Numbers:	1438996, 1302682, 1321047, 1553485
DBLP Key:	conf/micro/ZekanyRHLTM16
Author Comments:

IEEE/ACM International Symposium on Microarchitecture, MICRO 2016