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Documents authored by Wong, David

Found 2 Possible Name Variants:

Wong, David Chi-Leung

Document
DOI: 10.4230/DagSemProc.07361.1
07361 Abstracts Collection – Programming Models for Ubiquitous Parallelism

Authors: David Chi-Leung Wong, Albert Cohen, María J. Garzarán, Christian Lengauer, and Samuel P. Midkiff

Published in: Dagstuhl Seminar Proceedings, Volume 7361, Programming Models for Ubiquitous Parallelism (2008)

Abstract
From 02.09. to 07.09.2007, the Dagstuhl Seminar 07361 ``Programming Models for Ubiquitous Parallelism'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

David Chi-Leung Wong, Albert Cohen, María J. Garzarán, Christian Lengauer, and Samuel P. Midkiff. 07361 Abstracts Collection – Programming Models for Ubiquitous Parallelism. In Programming Models for Ubiquitous Parallelism. Dagstuhl Seminar Proceedings, Volume 7361, pp. 1-17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{wong_et_al:DagSemProc.07361.1,
  author =	{Wong, David Chi-Leung and Cohen, Albert and Garzar\'{a}n, Mar{\'\i}a J. and Lengauer, Christian and Midkiff, Samuel P.},
  title =	{{07361 Abstracts Collection – Programming Models for Ubiquitous Parallelism}},
  booktitle =	{Programming Models for Ubiquitous Parallelism},
  pages =	{1--17},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7361},
  editor =	{Albert Cohen and Mar{\'\i}a J. Garzar\'{a}n and Christian Lengauer and Samuel P. Midkiff},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07361.1},
  URN =		{urn:nbn:de:0030-drops-13770},
  doi =		{10.4230/DagSemProc.07361.1},
  annote =	{Keywords: Parallel programming models, transactional memory, languages, compilers, optimizations, architecture, automatic parallelization}
}
Document
DOI: 10.4230/DagSemProc.07361.2
07361 Introduction – Programming Models for Ubiquitous Parallelism

Authors: David Chi-Leung Wong, Albert Cohen, María J. Garzarán, Christian Lengauer, and Samuel P. Midkiff

Published in: Dagstuhl Seminar Proceedings, Volume 7361, Programming Models for Ubiquitous Parallelism (2008)

Abstract
The goal of the seminar is to present a broad view of the research challenges and ongoing efforts to improve productivity, scalability, efficiency and reliability of general-purpose and embedded parallel programming.
Cite as

David Chi-Leung Wong, Albert Cohen, María J. Garzarán, Christian Lengauer, and Samuel P. Midkiff. 07361 Introduction – Programming Models for Ubiquitous Parallelism. In Programming Models for Ubiquitous Parallelism. Dagstuhl Seminar Proceedings, Volume 7361, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{wong_et_al:DagSemProc.07361.2,
  author =	{Wong, David Chi-Leung and Cohen, Albert and Garzar\'{a}n, Mar{\'\i}a J. and Lengauer, Christian and Midkiff, Samuel P.},
  title =	{{07361 Introduction – Programming Models for Ubiquitous Parallelism}},
  booktitle =	{Programming Models for Ubiquitous Parallelism},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7361},
  editor =	{Albert Cohen and Mar{\'\i}a J. Garzar\'{a}n and Christian Lengauer and Samuel P. Midkiff},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07361.2},
  URN =		{urn:nbn:de:0030-drops-13736},
  doi =		{10.4230/DagSemProc.07361.2},
  annote =	{Keywords: Programming Models for Ubiquitous Parallelism}
}

Wong, David

Document
DOI: 10.4230/LIPIcs.DNA.27.1
Robust Digital Molecular Design of Binarized Neural Networks

Authors: Johannes Linder, Yuan-Jyue Chen, David Wong, Georg Seelig, Luis Ceze, and Karin Strauss

Published in: LIPIcs, Volume 205, 27th International Conference on DNA Computing and Molecular Programming (DNA 27) (2021)

Abstract
Molecular programming - a paradigm wherein molecules are engineered to perform computation - shows great potential for applications in nanotechnology, disease diagnostics and smart therapeutics. A key challenge is to identify systematic approaches for compiling abstract models of computation to molecules. Due to their wide applicability, one of the most useful abstractions to realize is neural networks. In prior work, real-valued weights were achieved by individually controlling the concentrations of the corresponding "weight" molecules. However, large-scale preparation of reactants with precise concentrations quickly becomes intractable. Here, we propose to bypass this fundamental problem using Binarized Neural Networks (BNNs), a model that is highly scalable in a molecular setting due to the small number of distinct weight values. We devise a noise-tolerant digital molecular circuit that compactly implements a majority voting operation on binary-valued inputs to compute the neuron output. The network is also rate-independent, meaning the speed at which individual reactions occur does not affect the computation, further increasing robustness to noise. We first demonstrate our design on the MNIST classification task by simulating the system as idealized chemical reactions. Next, we map the reactions to DNA strand displacement cascades, providing simulation results that demonstrate the practical feasibility of our approach. We perform extensive noise tolerance simulations, showing that digital molecular neurons are notably more robust to noise in the concentrations of chemical reactants compared to their analog counterparts. Finally, we provide initial experimental results of a single binarized neuron. Our work suggests a solid framework for building even more complex neural network computation.
Cite as

Johannes Linder, Yuan-Jyue Chen, David Wong, Georg Seelig, Luis Ceze, and Karin Strauss. Robust Digital Molecular Design of Binarized Neural Networks. In 27th International Conference on DNA Computing and Molecular Programming (DNA 27). Leibniz International Proceedings in Informatics (LIPIcs), Volume 205, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{linder_et_al:LIPIcs.DNA.27.1,
  author =	{Linder, Johannes and Chen, Yuan-Jyue and Wong, David and Seelig, Georg and Ceze, Luis and Strauss, Karin},
  title =	{{Robust Digital Molecular Design of Binarized Neural Networks}},
  booktitle =	{27th International Conference on DNA Computing and Molecular Programming (DNA 27)},
  pages =	{1:1--1:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-205-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{205},
  editor =	{Lakin, Matthew R. and \v{S}ulc, Petr},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DNA.27.1},
  URN =		{urn:nbn:de:0030-drops-146685},
  doi =		{10.4230/LIPIcs.DNA.27.1},
  annote =	{Keywords: Molecular Computing, Neural Network, Binarized Neural Network, Digital Logic, DNA, Strand Displacement}
}
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