Projects
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Current projects
| Project title: |
Computing with Biomolecules: From Network Motifs to Complex and Adaptive Systems |
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| Project website: |
https://digamma.cs.unm.edu/wiki/bin/view/McogPublicWeb/GrantsNSF1028238 | |
| Description: | Can molecules be organized, or even self-organize, to perform complex tasks? This question seemingly should have an answer in the affirmative, because all the world around us, and especially the living things in it, is a mass of molecules. Yet the complexity of molecular systems, or networks, that chemistry has achieved to date pales in comparison with electronics and computers. A team of chemists, engineers, and computer scientists join forces to explore how molecules can be harnessed to achieve complex behaviors, including simple forms of computation, adaptation, and learning. They will work to discover the engineering principles needed to make large and useful molecular circuits. They will start by building networks of DNA enzymes, where actions of one enzyme stimulate another, just as protein enzymes stimulate one another in a living cell. They will build networks that can detect patterns of change in the environment; for example, the strength and the frequency of a periodic stimulation. Ultimately, they will build large networks that embody the ability to learn (memorize and generalize) patterns of past stimulation, and respond to new conditions in the environment accordingly. The project will develop biocompatible, DNA molecular sensors and actuators that will be of immediate use in pathogen detection, non-invasive diagnostics, and intelligent therapeutics. As the project progresses, these will be combined with ever more capable biomolecular learning machines we will develop, but they can also be used independently by other researchers or medical doctors. The project will reach out to underserved communities in Oregon, New Mexico, and inner-city New York, providing high-school students and undergraduates an opportunity to engage in interdisciplinary scientific research. |
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| Collaborators: | Steven Graves, UNM Terran Lane, UNM Darko Stefanovic, UNM Milan Stojanovic, Columbia University Christof Teuscher, PSU |
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| Funding: | National Science Foundation (NSF), Cyber-enabled Discovery and Innovation (CDI), Type II award |
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| NSF grant number: |
1028120 |
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| Duration: |
Oct 1, 2010 - Sep 30, 2014 |
| Project title: |
Inference at the Nanoscale |
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| Description: | The goal of this project is to develop a new inference-based information processing structure that performs probabilistic computing using radically new nanoscale devices. Our approach exploits the analog, time-dependent properties of such devices, and their massive parallelism. By doing so, such a computing structure will be more efficient and scalable than by using more traditional digital hardware. This approach is the first that we are aware of to include time-dependent circuit elements to build analog associative memories that approximate Bayesian inference, and which are, in turn, assembled into complex networks that capture higher order structure in streams of data. Our ultimate goal is to use these circuits to develop hybrid CMOS / molecular scale implementations of a Field Adaptable Bayesian Array (FABA), which, we believe, has the potential to be a key component for Cyber-Enabled discovery. Two key developments then are a design exploration methodology for such devices, and a massively parallel architecture for data capture and inference. In this work we explore a new paradigm for using nanoscale electronics for emerging applications by starting with the “top-down” system requirements rather than by finding applications for new device concepts (“bottom-up”). We address Cyber-Enabled discovery in two ways. The first concerns the design of analog circuits based on complex nano and molecular scale devices with time-varying properties. Designing analog nano-electronic circuits that perform inference through space and time and which consist of dynamic components (such as mem-resistance and mem-capacitance) is extraordinarily difficult. This is particularly true when one considers the wide range of complex devices that are being developed in laboratories around the world for nano and molecular scale electronics. For this effort we have defined an Exploration Methodology that combines multiple levels of abstraction and evolvable computation. As the semiconductor industry struggles with where to go next, the work proposed here may provide insight into radical new approaches to architecture, circuits and devices. This research will ultimately benefit society by enhancing human cognition and generating new knowledge from the wealth of heterogeneous digital data society has to deal with. |
| Collaborators: | Dan Hammerstrom, PSU Dmitry Strukov, UC Santa Barbara Christof Teuscher, PSU |
| Funding: | National Science Foundation (NSF), Cyber-enabled Discovery and Innovation (CDI), Type II award |
| NSF grant number: |
1028378 |
| Duration: |
Sep 15, 2010 - Aug 31, 2014 |
| Project title: |
Optimization of Hierarchical and Heterogeneous Network-on-Chip (NoC) Architectures |
| Project acronym: |
CNEA |
| Description: | The goal of this project is to optimize hierarchical and heterogeneous NoC architectures with long-range links. This will allow to find non-classical interconnect architectures for multi-core chips by drawing inspiration from natural complex networks that minimize resource consumption while optimizing relevant performance metrics, such as latency, throughput, power and area overhead. We apply metaheuristic algorithms to find optimal solutions. |
| Collaborators: | Partha Pande, Washington State University |
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| Project title: |
Robustness and Damage Spreading in Self-Organized Nanoscale Electronics |
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| Description: | We propose to study and model the robustness and damage spreading in self-organized nanoscale electronics. Molecular and nanoscale electronics seeks to build devices to implement computation by using collections of molecules. It is generally expected that such emerging computing devices will be self-assembled in a bottom-up and hierarchical way from vast numbers of simple, densely arranged components that exhibit high failure rates, are relatively slow, and connected in a disordered way. We will pursue an interdisciplinary approach by building realistic models and exploring key design trade-offs. In particular, we will model networks of randomly assembled nanowires and carbon nanotubes and study how local damage affects the overall system robustness and performance. The models and simulations will be compared with both models and data from gene regulatory networks with the goal to unveil underlying design principles that lead to robust systems. The research will advance the state of the art by drawing inspiration from natural complex systems. The outcomes will lay the foundations for a new research effort in understanding and designing man-made complex, emerging information processing devices. From a broader perspective, our work contributes to the question of how to engineer a system we don’t fully understand. |
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| Funding: | Office of Research and Sponsored Projects (ORSP) |
| Project title: |
Designing Communication Methods for Bottom-Up Self-Assembled Nanowire Networks of Emerging Computer Architectures |
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NANONETS |
| Description: | Our goal is to engineer novel computer architectures based on self-assembled nano- components and through an integrated experimental, simulation-based, and theoretical approach. The bottom-up design approach is more fabrication-friendly, cheaper, and would eventually scale up to more complex systems compared to today’s top-down designs. We adopt a network- and system-on-chip-based approach and focus on the interconnect challenge because interconnects have become more important than the transistors as a limiting factor of performance on modern chips. The challenge we address is twofold: (1) master the particular technology of self-assembling conductive silver nanowires that densely interconnect traditional silicon components, and (2) develop appropriate schemes that will allow reliable communication in such an irregular, heterogeneous, and unreliable network. The research is guided by the following main questions: (1) What connectivity graphs can we obtain, and what are the control parameters for the self-assembly process? (2) How can we reliably and efficiently communicate in such irregular networks, given a set of limited resources and a lack of global information and connectivity? |
| Collaborators: | Hsing-Lin Wang, Los Alamos National Laboratory (LANL) Marian Anghel, Los Alamos National Laboratory (LANL) Hou-Tong Chen, Los Alamos National Laboratory (LANL) |
| Funding: | DOE LDRD program |
| Project title: |
Random Automata Architectures |
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| Description: | The goal of this project is to assess and design emerging computing architectures based on unstructured physical devices. Molecular and nanoscale electronics seeks to build devices to implement computation by using collections of molecules. It is generally expected that such emerging computing devices will be built in a bottom-up and hierarchical way from vast numbers of simple, densely arranged components that exhibit high failure rates, are relatively slow, and connected in a disordered way |
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| Project title: |
Developmental mechanisms for massive-scale computing assemblies |
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| Description: | Nature has evolved multiple adaptation techniques on multiple time-scales, which help organisms to be resilient against changes in the environment. One of the basic mechanisms behind the resilience of biological organisms is cellular division, i.e., the ability of the cells to self-replicate. Self-replication in computing machines has been explore first by John von Neumann in the 1950s, with more recent research in the 1980s by Chris Langton. The goal of this project is to propose developmental mechanisms that can be applied to future and emerging nano-scale electronics. |
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| Project title: |
Adaptive Control of Self-Assembled Computing Systems |
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| Description: |
Molecular and nanoscale electronics seeks to build devices to implement computation by using collections of molecules. It is generally expected that such emerging computing devices will be built in a bottom-up and hierarchical way from vast numbers of simple, densely arranged components that exhibit high failure rates, are relatively slow, and connected in a disordered way. Such devices are the prototypical example of complex systems that show emergent behavior not obvious from considering the separate components. They are not programmable by standard means because the reductionist approach fails. The research questions we address are as following: What internal system configuration results in a desired input-output mapping? How can we adapt the system by an algorithm that acts on the control signals to reach such an internal configuration? How can we make the control scalable and robust against certain component failures? |
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| Project title: |
Complex Materials Networks Assembled From Simple Nanoscale Building Blocks for Energy Conversion and Information Processing |
| Project acronym: |
NANOSTRUCT |
| Description: | We investigate complex materials networks assembled from simple nanoscale building blocks for energy conversion (solar cells, fuel cells, batteries) and information processing (future computers). Unveiling the design principles and both fundamental and practical limits has promise for a lasting impact on basic nanoscience and the important applied problems of addressing low-carbon energy conversion and building novel computing devices. |
| Collaborators: | Dr. Jessika Trancik, MIT |
| Funding: | Center for Integrated Nanotechnologies (CINT), Los Alamos National Lab, Sandia National Labs |
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Past projects
