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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.

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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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DOI: 10.4230/LIPIcs.SNAPL.2015.262

Hardware-Software Co-Design: Not Just a Cliché

Authors: Adrian Sampson, James Bornholt, and Luis Ceze

Published in: LIPIcs, Volume 32, 1st Summit on Advances in Programming Languages (SNAPL 2015)

Abstract

The age of the air-tight hardware abstraction is over. As the computing ecosystem moves beyond the predictable yearly advances of Moore's Law, appeals to familiarity and backwards compatibility will become less convincing: fundamental shifts in abstraction and design will look more enticing. It is time to embrace hardware-software co-design in earnest, to cooperate between programming languages and architecture to upend legacy constraints on computing. We describe our work on approximate computing, a new avenue spanning the system stack from applications and languages to microarchitectures. We reflect on the challenges and successes of approximation research and, with these lessons in mind, distill opportunities for future hardware-software co-design efforts.

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Adrian Sampson, James Bornholt, and Luis Ceze. Hardware-Software Co-Design: Not Just a Cliché. In 1st Summit on Advances in Programming Languages (SNAPL 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 32, pp. 262-273, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{sampson_et_al:LIPIcs.SNAPL.2015.262,
  author =	{Sampson, Adrian and Bornholt, James and Ceze, Luis},
  title =	{{Hardware-Software Co-Design: Not Just a Clich\'{e}}},
  booktitle =	{1st Summit on Advances in Programming Languages (SNAPL 2015)},
  pages =	{262--273},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-80-4},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{32},
  editor =	{Ball, Thomas and Bodík, Rastislav and Krishnamurthi, Shriram and Lerner, Benjamin S. and Morriset, Greg},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SNAPL.2015.262},
  URN =		{urn:nbn:de:0030-drops-50301},
  doi =		{10.4230/LIPIcs.SNAPL.2015.262},
  annote =	{Keywords: approximation, co-design, architecture, verification}
}

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Documents authored by Ceze, Luis

Robust Digital Molecular Design of Binarized Neural Networks

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Hardware-Software Co-Design: Not Just a Cliché

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