In this paper, a generic technique for mapping heterogeneous task graphs onto heterogeneous syste... more In this paper, a generic technique for mapping heterogeneous task graphs onto heterogeneous system graphs is presented. The task and system graphs studied in this paper have nonuniform computation and communication weights associated with the nodes and the edges. Two clustering algorithms have been proposed which can be used to obtain a multilayer clustered graph called a Spec graph from a given task graph and a multilayer clustered graph called a Rep graph from a given system graph. We present a mapping algorithm which produces a suboptimal matching of a given Spec graph containing M task modules, onto a Rep graph of N processors, in O(MP) fame, where P=max(M,N). Our experimental results indicate that our mapping algorithm is the fastest one and generates results which are better than, or similar to, those of other leading techniques which work only for restricted task or system graphs.
In 1959, Nobel laureate Richard Feynman posed this question to his fellow physicists: &#x... more In 1959, Nobel laureate Richard Feynman posed this question to his fellow physicists: ''Why cannot we write the entire 24 volumes of the Encyclopedia Britannica on the head of a pin?'' In that lecture, aptly named ''There's Plenty of Room at the Bottom,'' Feynman challenged ...
Department of Electrical Engineering University of California, Los Angeles Los Angeles, CA 90095-... more Department of Electrical Engineering University of California, Los Angeles Los Angeles, CA 90095-1594 (310) 825-2647 maryew@ee.ucla.edu, jonlau@ucla.edu, shiva_n@ee.ucla.edu , dshen727@ucla.edu ... 1 The authors are listed alphabetically by last name.
Department of Electrical Engineering University of California, Los Angeles Los Angeles, CA 90095-... more Department of Electrical Engineering University of California, Los Angeles Los Angeles, CA 90095-1594 (310) 825-2647 maryew, ahit, shiva_n, wang @ee.ucla.edu ... 1 The authors are listed in alphabetical order. ... Abstract In this paper, we present three hierarchical multi- ...
A generic nanoscale computing model is presented in this paper. The model consists of a collectio... more A generic nanoscale computing model is presented in this paper. The model consists of a collection of fully interconnected nanoscale computing modules, where each module is a cube of cells made out of quantum dots, spins, or molecules. The cells dynamically switch between two states by quantum interactions among their neighbors in all three dimensions. This paper includes a brief introduction to the field of nanotechnology from a computing point of view and presents a set of preliminary architectural designs for
This is a short communication where we present a theoretical model of a swarm of wireless robots ... more This is a short communication where we present a theoretical model of a swarm of wireless robots that can be used for cellular-level diagnosis and treatment of a variety of life threatening diseases such as cancer. Based on this model, we illustrate a distributed position and orientation tracking algorithm that constructs digitized images from a set of pixels transmitted by the robots of the swarm model that are in motion. Simulation results are also presented.
In this paper, we propose using a new nanoscale spin-wave-based architecture for implementing neu... more In this paper, we propose using a new nanoscale spin-wave-based architecture for implementing neural networks. We show that this architecture can efficiently realize highly interconnected neural network models such as the Hopfield model. In our proposed architecture, no point-to-point interconnection is required, so unlike standard VLSI design, no fan-in/fan-out constraint limits the interconnectivity. Using spin-waves, each neuron could broadcast to all other neurons simultaneously and similarly a neuron could concurrently receive and process multiple data. Therefore in this architecture, the total weighted sum to each neuron can be computed by the sum of the values from all the incoming waves to that neuron. In addition, using the superposition property of waves, this computation can be done in O(1) time, and neurons can update their states quite rapidly.
In this paper, a generic technique for mapping heterogeneous task graphs onto heterogeneous syste... more In this paper, a generic technique for mapping heterogeneous task graphs onto heterogeneous system graphs is presented. The task and system graphs studied in this paper have nonuniform computation and communication weights associated with the nodes and the edges. Two clustering algorithms have been proposed which can be used to obtain a multilayer clustered graph called a Spec graph from a given task graph and a multilayer clustered graph called a Rep graph from a given system graph. We present a mapping algorithm which produces a suboptimal matching of a given Spec graph containing M task modules, onto a Rep graph of N processors, in O(MP) fame, where P=max(M,N). Our experimental results indicate that our mapping algorithm is the fastest one and generates results which are better than, or similar to, those of other leading techniques which work only for restricted task or system graphs.
In 1959, Nobel laureate Richard Feynman posed this question to his fellow physicists: &#x... more In 1959, Nobel laureate Richard Feynman posed this question to his fellow physicists: ''Why cannot we write the entire 24 volumes of the Encyclopedia Britannica on the head of a pin?'' In that lecture, aptly named ''There's Plenty of Room at the Bottom,'' Feynman challenged ...
Department of Electrical Engineering University of California, Los Angeles Los Angeles, CA 90095-... more Department of Electrical Engineering University of California, Los Angeles Los Angeles, CA 90095-1594 (310) 825-2647 maryew@ee.ucla.edu, jonlau@ucla.edu, shiva_n@ee.ucla.edu , dshen727@ucla.edu ... 1 The authors are listed alphabetically by last name.
Department of Electrical Engineering University of California, Los Angeles Los Angeles, CA 90095-... more Department of Electrical Engineering University of California, Los Angeles Los Angeles, CA 90095-1594 (310) 825-2647 maryew, ahit, shiva_n, wang @ee.ucla.edu ... 1 The authors are listed in alphabetical order. ... Abstract In this paper, we present three hierarchical multi- ...
A generic nanoscale computing model is presented in this paper. The model consists of a collectio... more A generic nanoscale computing model is presented in this paper. The model consists of a collection of fully interconnected nanoscale computing modules, where each module is a cube of cells made out of quantum dots, spins, or molecules. The cells dynamically switch between two states by quantum interactions among their neighbors in all three dimensions. This paper includes a brief introduction to the field of nanotechnology from a computing point of view and presents a set of preliminary architectural designs for
This is a short communication where we present a theoretical model of a swarm of wireless robots ... more This is a short communication where we present a theoretical model of a swarm of wireless robots that can be used for cellular-level diagnosis and treatment of a variety of life threatening diseases such as cancer. Based on this model, we illustrate a distributed position and orientation tracking algorithm that constructs digitized images from a set of pixels transmitted by the robots of the swarm model that are in motion. Simulation results are also presented.
In this paper, we propose using a new nanoscale spin-wave-based architecture for implementing neu... more In this paper, we propose using a new nanoscale spin-wave-based architecture for implementing neural networks. We show that this architecture can efficiently realize highly interconnected neural network models such as the Hopfield model. In our proposed architecture, no point-to-point interconnection is required, so unlike standard VLSI design, no fan-in/fan-out constraint limits the interconnectivity. Using spin-waves, each neuron could broadcast to all other neurons simultaneously and similarly a neuron could concurrently receive and process multiple data. Therefore in this architecture, the total weighted sum to each neuron can be computed by the sum of the values from all the incoming waves to that neuron. In addition, using the superposition property of waves, this computation can be done in O(1) time, and neurons can update their states quite rapidly.
Uploads
Papers