Mary Mehrnoosh Eshaghian-Wilner - Academia.edu (original) (raw)

Papers by Mary Mehrnoosh Eshaghian-Wilner

Research paper thumbnail of Tools for Design and Mapping of Portable Parallel Programs

Research paper thumbnail of A Sub-optimal Heterogeneous Mapping

Research paper thumbnail of Cluster-M Paradigms for High-Order Heterogeneous Procedural Specification Computing

Proceedings. Workshop on Heterogeneous Processing

In this paper, we study an approach called superconcurrency for identifying and employing an opti... more In this paper, we study an approach called superconcurrency for identifying and employing an optimal, heterogeneous suite of processors to solve traditional supercomputing problems. We discuss mainly two components of superconcurrency: Optimal selection theory, and Parallel programming tools. We briefly describe a number of existing parallel programming tools and present a parallel programming model called Cluster-M. This model efficiently provides a programming paradigm for high-order heterogeneous procedural specification computing. Using Cluster-M, portable software can be generated which may be mapped onto various configurations of heterogeneous supercomputing.

Research paper thumbnail of Mapping heterogeneous task graphs onto heterogeneous system graphs

Proceedings Sixth Heterogeneous Computing Workshop (HCW'97)

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.

Research paper thumbnail of Efficient Parallel Computing with an Optically Interconnected Reconfigurable Mesh

Journal of Parallel and Distributed Computing - JPDC, 2001

Research paper thumbnail of Resource requirements for digital computations on electrooptical systems

Applied Optics, 1991

In this paper, we study the resource requirements of electro-optical organizations in solving com... more In this paper, we study the resource requirements of electro-optical organizations in solving computationally intensive problems such as 2-D image convolution. Using a generic model of parallel computation with optical interconnects called OMC, we derive the relationships for information transfer versus space/time tradeo s in solving a problem. Irrespective of the I/O scheme and the order of computation, we show that a lower bound of (nw) memory space represents the minimum hardware required for convolving a w w kernel with a n n image, if the input bits are given to the system only once.

Research paper thumbnail of An Information Power Grid Resource Management Tool

Computación Y Sistemas, Dec 31, 2001

Heterogeneous Computing (HC) is defined as a special form of parallel and distributed computing. ... more Heterogeneous Computing (HC) is defined as a special form of parallel and distributed computing. Computations are carried out using a single autonomous computer operating in both SIMD and MIMD modes, or using a number of connected autonomous computers (a.k.a. Cluster Computing). Information Power Grid (IPG) a is form of HC in which high performance computers located at geo-L. M.

Research paper thumbnail of Comparative study of variations in quantum approximate optimization algorithms for the Traveling Salesman Problem

arXiv (Cornell University), Jul 14, 2023

The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer s... more The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this paper, we tackle the TSP using the quantum approximate optimization algorithm (QAOA) approach by formulating it as an optimization problem. By adopting an improved qubit encoding strategy and a layerwise learning optimization protocol, we present numerical results obtained from the gate-based digital quantum simulator, specifically targeting TSP instances with 3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixer designs, considering their performances in terms of numerical accuracy and optimization cost. Notably, we find a well-balanced QAOA mixer design exhibits more promising potential for gate-based simulators and realistic quantum devices in the long run, an observation further supported by our noise model simulations. Furthermore, we investigate the sensitivity of the simulations to the TSP graph. Overall, our simulation results show the digital quantum simulation of problem-inspired ansatz is a successful candidate for finding optimal TSP solutions.

Research paper thumbnail of An Introduction to Nanocomputing

John Wiley & Sons, Inc. eBooks, Nov 24, 2009

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 ...

Research paper thumbnail of Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), May 1, 2022

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage a... more The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor's algorithm, weighted sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM's superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands' superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges-relevant to signed, unsigned, modular and non-modular versions-that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. We analyze performance trends in our results and speculate on possible future development within this computational paradigm.

Research paper thumbnail of Graph Formations of Partial-Order Multiple-Sequence Alignments Using Nano and Micro-Scale Reconfigurable Meshes

CDES, 2006

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.

Research paper thumbnail of Q-CASA Invited Speaker Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic with Signed and Unsigned Integers

2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

The Quantum Fourier Transform (QFT) grants competitive advantages for performing arithmetic opera... more The Quantum Fourier Transform (QFT) grants competitive advantages for performing arithmetic operations on quantum computers, and presents a potential route towards a numerical quantumcomputational paradigm. Qubit simulation allows us to better gauge what will be possible as the technology improves and identify strategies for early implementations on noisy quantum devices. In this presentation, I will review an implementation of QFT-based unsigned integer addition and multiplication, and present their performance evaluation using noise models based on IBM's superconducting qubit architecture. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, the number of the arithmetic operands' superposed integer states, and level of circuit approximation in the QFT. I will then extend the discussion to how this approach may be implemented for signed quantum Fourier arithmetic and offer some preliminary results.

Research paper thumbnail of Comparative study on the variations of quantum approximate optimization algorithms to the Traveling Salesman Problem

2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer s... more The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this paper, we tackle the TSP using the quantum approximate optimization algorithm (QAOA) approach by formulating it as an optimization problem. By adopting an improved qubit encoding strategy and a layerwise learning optimization protocol, we present numerical results obtained from the gate-based digital quantum simulator, specifically targeting TSP instances with 3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixer designs, considering their performances in terms of numerical accuracy and optimization cost. Notably, we find a well-balanced QAOA mixer design exhibits more promising potential for gate-based simulators and realistic quantum devices in the long run, an observation further supported by our noise model simulations. Furthermore, we investigate the sensitivity of the simulations to the TSP graph. Overall, our simulation results show the digital quantum simulation of problem-inspired ansatz is a successful candidate for finding optimal TSP solutions.

Research paper thumbnail of Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage a... more The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor's algorithm, weighted sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM's superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands' superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges-relevant to signed, unsigned, modular and non-modular versions-that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. We analyze performance trends in our results and speculate on possible future development within this computational paradigm.

Research paper thumbnail of Bio-Inspired and Nanoscale Integrated Computing

Research paper thumbnail of Proceedings of the 2006 International Conference on Privacy, Security and Trust Bridge the Gap Between PST Technologies and Business Services - PST '06

Research paper thumbnail of Hierarchical Multi-Scale Architectures with Spin Waves

International Conference on Computer Design, 2006

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- ...

Research paper thumbnail of The Architectural Design of a Nano-Scale Computing Model

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

Research paper thumbnail of Real-time cellular-level imaging and medical treatment with a swarm of wireless multifunctional robots

The Journal of Supercomputing, 2021

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.

Research paper thumbnail of Emulation of Neural Networks on a Nanoscale Spin-Wave Architecture 1

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.

Research paper thumbnail of Tools for Design and Mapping of Portable Parallel Programs

Research paper thumbnail of A Sub-optimal Heterogeneous Mapping

Research paper thumbnail of Cluster-M Paradigms for High-Order Heterogeneous Procedural Specification Computing

Proceedings. Workshop on Heterogeneous Processing

In this paper, we study an approach called superconcurrency for identifying and employing an opti... more In this paper, we study an approach called superconcurrency for identifying and employing an optimal, heterogeneous suite of processors to solve traditional supercomputing problems. We discuss mainly two components of superconcurrency: Optimal selection theory, and Parallel programming tools. We briefly describe a number of existing parallel programming tools and present a parallel programming model called Cluster-M. This model efficiently provides a programming paradigm for high-order heterogeneous procedural specification computing. Using Cluster-M, portable software can be generated which may be mapped onto various configurations of heterogeneous supercomputing.

Research paper thumbnail of Mapping heterogeneous task graphs onto heterogeneous system graphs

Proceedings Sixth Heterogeneous Computing Workshop (HCW'97)

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.

Research paper thumbnail of Efficient Parallel Computing with an Optically Interconnected Reconfigurable Mesh

Journal of Parallel and Distributed Computing - JPDC, 2001

Research paper thumbnail of Resource requirements for digital computations on electrooptical systems

Applied Optics, 1991

In this paper, we study the resource requirements of electro-optical organizations in solving com... more In this paper, we study the resource requirements of electro-optical organizations in solving computationally intensive problems such as 2-D image convolution. Using a generic model of parallel computation with optical interconnects called OMC, we derive the relationships for information transfer versus space/time tradeo s in solving a problem. Irrespective of the I/O scheme and the order of computation, we show that a lower bound of (nw) memory space represents the minimum hardware required for convolving a w w kernel with a n n image, if the input bits are given to the system only once.

Research paper thumbnail of An Information Power Grid Resource Management Tool

Computación Y Sistemas, Dec 31, 2001

Heterogeneous Computing (HC) is defined as a special form of parallel and distributed computing. ... more Heterogeneous Computing (HC) is defined as a special form of parallel and distributed computing. Computations are carried out using a single autonomous computer operating in both SIMD and MIMD modes, or using a number of connected autonomous computers (a.k.a. Cluster Computing). Information Power Grid (IPG) a is form of HC in which high performance computers located at geo-L. M.

Research paper thumbnail of Comparative study of variations in quantum approximate optimization algorithms for the Traveling Salesman Problem

arXiv (Cornell University), Jul 14, 2023

The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer s... more The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this paper, we tackle the TSP using the quantum approximate optimization algorithm (QAOA) approach by formulating it as an optimization problem. By adopting an improved qubit encoding strategy and a layerwise learning optimization protocol, we present numerical results obtained from the gate-based digital quantum simulator, specifically targeting TSP instances with 3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixer designs, considering their performances in terms of numerical accuracy and optimization cost. Notably, we find a well-balanced QAOA mixer design exhibits more promising potential for gate-based simulators and realistic quantum devices in the long run, an observation further supported by our noise model simulations. Furthermore, we investigate the sensitivity of the simulations to the TSP graph. Overall, our simulation results show the digital quantum simulation of problem-inspired ansatz is a successful candidate for finding optimal TSP solutions.

Research paper thumbnail of An Introduction to Nanocomputing

John Wiley & Sons, Inc. eBooks, Nov 24, 2009

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 ...

Research paper thumbnail of Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), May 1, 2022

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage a... more The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor's algorithm, weighted sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM's superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands' superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges-relevant to signed, unsigned, modular and non-modular versions-that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. We analyze performance trends in our results and speculate on possible future development within this computational paradigm.

Research paper thumbnail of Graph Formations of Partial-Order Multiple-Sequence Alignments Using Nano and Micro-Scale Reconfigurable Meshes

CDES, 2006

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.

Research paper thumbnail of Q-CASA Invited Speaker Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic with Signed and Unsigned Integers

2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

The Quantum Fourier Transform (QFT) grants competitive advantages for performing arithmetic opera... more The Quantum Fourier Transform (QFT) grants competitive advantages for performing arithmetic operations on quantum computers, and presents a potential route towards a numerical quantumcomputational paradigm. Qubit simulation allows us to better gauge what will be possible as the technology improves and identify strategies for early implementations on noisy quantum devices. In this presentation, I will review an implementation of QFT-based unsigned integer addition and multiplication, and present their performance evaluation using noise models based on IBM's superconducting qubit architecture. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, the number of the arithmetic operands' superposed integer states, and level of circuit approximation in the QFT. I will then extend the discussion to how this approach may be implemented for signed quantum Fourier arithmetic and offer some preliminary results.

Research paper thumbnail of Comparative study on the variations of quantum approximate optimization algorithms to the Traveling Salesman Problem

2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer s... more The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this paper, we tackle the TSP using the quantum approximate optimization algorithm (QAOA) approach by formulating it as an optimization problem. By adopting an improved qubit encoding strategy and a layerwise learning optimization protocol, we present numerical results obtained from the gate-based digital quantum simulator, specifically targeting TSP instances with 3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixer designs, considering their performances in terms of numerical accuracy and optimization cost. Notably, we find a well-balanced QAOA mixer design exhibits more promising potential for gate-based simulators and realistic quantum devices in the long run, an observation further supported by our noise model simulations. Furthermore, we investigate the sensitivity of the simulations to the TSP graph. Overall, our simulation results show the digital quantum simulation of problem-inspired ansatz is a successful candidate for finding optimal TSP solutions.

Research paper thumbnail of Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage a... more The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor's algorithm, weighted sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM's superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands' superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges-relevant to signed, unsigned, modular and non-modular versions-that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. We analyze performance trends in our results and speculate on possible future development within this computational paradigm.

Research paper thumbnail of Bio-Inspired and Nanoscale Integrated Computing

Research paper thumbnail of Proceedings of the 2006 International Conference on Privacy, Security and Trust Bridge the Gap Between PST Technologies and Business Services - PST '06

Research paper thumbnail of Hierarchical Multi-Scale Architectures with Spin Waves

International Conference on Computer Design, 2006

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- ...

Research paper thumbnail of The Architectural Design of a Nano-Scale Computing Model

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

Research paper thumbnail of Real-time cellular-level imaging and medical treatment with a swarm of wireless multifunctional robots

The Journal of Supercomputing, 2021

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.

Research paper thumbnail of Emulation of Neural Networks on a Nanoscale Spin-Wave Architecture 1

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.