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Michael Wainberg

dblp:147/7970 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2016
0000-0001-6061-0239ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Learning theory · 67% Kernel, tree and ensemble methods · 33%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory › classification › classifier evaluation
classifier comparison
0.212016
Are Random Forests Truly the Best Classifiers? · J. Mach. Learn. Res. 2016
Machine learning › Kernel, tree and ensemble methods › ensemble learning › tree ensembles
random forest
0.212016
Are Random Forests Truly the Best Classifiers? · J. Mach. Learn. Res. 2016
Electronic design automation › design automation tools
FPGA CAD
0.212015
Robust Optimization of Multiple Timing Constraints · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2015
Electronic design automation › physical design
timing optimization
0.212015
Robust Optimization of Multiple Timing Constraints · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2015
Electronic design automation
physical design
0.112015
Robust Optimization of Multiple Timing Constraints · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2015

Methods — techniques the papers use, named apart from their topics

statistical testing · 0.2cross-validation · 0.2robust optimization · 0.2
YearPublicationVenuePosition
2016 Are Random Forests Truly the Best Classifiers?
abstract
The JMLR study Do we need hundreds of classifiers to solve real world classification problems? benchmarks 179 classifiers in 17 families on 121 data sets from the UCI repository and claims that “the random forest is clearly the best family of classifier”. In this response, we show that the study's results are biased by the lack of a held-out test set and the exclusion of trials with errors. Further, the study's own statistical tests indicate that random forests do not have significantly higher percent accuracy than support vector machines and neural networks, calling into question the conclusion that random forests are the best classifiers.
Michael Wainberg, Babak Alipanahi, Brendan J. Frey
J. Mach. Learn. Res.1
2015 Robust Optimization of Multiple Timing Constraints
abstract
Modern field-programmable gate array (FPGA) circuit designs often contain multiple clocks and complex timing constraints, and achieving these constraints requires timing optimization at all stages of the computer-aided design (CAD) flow. To our knowledge, no prior published work has either described or quantitatively evaluated how to compute connection timing criticalities for circuits with multiple timing constraints in order to best guide CAD optimization algorithms. While single-clock techniques have a simple extension to multi-clock circuits, this formulation is not robust for circuits with multiple constraints of different magnitudes, or impossible constraints. We describe a robust method of timing optimization for circuits with multiple timing constraints, implemented in the open-source versatile place and route FPGA CAD tool. Our formulation can optimize multiple constraints well, even in the case where some constraints are impossible, and achieves over 20% greater clock speed with aggressive constraints than a straight-forward extension of single-clock work.
Michael Wainberg, Vaughn Betz
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.1
2014 VTR 7.0: Next Generation Architecture and CAD System for FPGAs
abstract
Exploring architectures for large, modern FPGAs requires sophisticated software that can model and target hypothetical devices. Furthermore, research into new CAD algorithms often requires a complete and open source baseline CAD flow. This article describes recent advances in the open source Verilog-to-Routing (VTR) CAD flow that enable further research in these areas. VTR now supports designs with multiple clocks in both timing analysis and optimization. Hard adder/carry logic can be included in an architecture in various ways and significantly improves the performance of arithmetic circuits. The flow now models energy consumption, an increasingly important concern. The speed and quality of the packing algorithms have been significantly improved. VTR can now generate a netlist of the final post-routed circuit which enables detailed simulation of a design for a variety of purposes. We also release new FPGA architecture files and models that are much closer to modern commercial architectures, enabling more realistic experiments. Finally, we show that while this version of VTR supports new and complex features, it has a 1.5× compile time speed-up for simple architectures and a 6× speed-up for complex architectures compared to the previous release, with no degradation to timing or wire-length quality.
Jason Luu, Jeffrey B. Goeders, Michael Wainberg, Andrew Somerville, Thien Yu, Konstantin Nasartschuk, Miad Nasr, Tim Liu, Nooruddin Ahmed, Kenneth B. Kent, Jason Helge Anderson, Jonathan Rose, Vaughn Betz
ACM Trans. Reconfigurable Technol. Syst.3