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  • Nanophotonic spin-glass for realization of a coherent Ising machine
    arXiv.cs.ET Pub Date : 2020-03-25
    Yoshitomo Okawachi; Mengjie Yu; Jae K. Jang; Xingchen Ji; Yun Zhao; Bok Young Kim; Michal Lipson; Alexander L. Gaeta

    The need for solving optimization problems is prevalent in a wide range of physical applications, including neuroscience, network design, biological systems, socio-economics, and chemical reactions. Many of these are classified as non-deterministic polynomial-time (NP) hard and thus become intractable to solve as the system scales to a large number of elements. Recent research advances in photonics

  • Information and Communication Theoretical Understanding and Treatment of Spinal Cord Injuries: State-of-the-art and Research Challenges
    arXiv.cs.ET Pub Date : 2020-03-26
    Ozgur B. Akan; Hamideh Ramezani; Meltem Civas; Oktay Cetinkaya; Bilgesu A. Bilgin; Naveed A. Abbasi

    Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication

  • Redesigning Photonic Interconnects with Silicon-on-Sapphire Device Platform for Ultra-Low-Energy On-Chip Communication
    arXiv.cs.ET Pub Date : 2020-02-28
    Venkata Sai Praneeth Karempudi; Sairam Sri Vatsavai; Ishan Thakkar

    Traditional silicon-on-insulator (SOI) platform based on-chip photonic interconnects have limited energy-bandwidth scalability due to the optical non-linearity induced power constraints of the constituent photonic devices. In this paper, we propose to break this scalability barrier using a new silicon-on-sapphire (SOS) based photonic device platform. Our physical-layer characterization results show

  • Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture
    arXiv.cs.ET Pub Date : 2020-03-24
    Alvaro Velasquez; Christopher H. Bennett; Naimul Hassan; Wesley H. Brigner; Otitoaleke G. Akinola; Jean Anne C. Incorvia; Matthew J. Marinella; Joseph S. Friedman

    We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture. The proposed approach leverages the three-terminal structure of domain-wall magnetic tunnel junction devices to establish a feedback loop that serves to train such devices when they are used as synapses in a neuromorphic computing architecture.

  • An experimental proof that resistance-switching memories are not memristors
    arXiv.cs.ET Pub Date : 2019-09-16
    J. Kim; Y. V. Pershin; M. Yin; T. Datta; M. Di Ventra

    It has been suggested that all resistive-switching memory cells are memristors. The latter are hypothetical, ideal devices whose resistance, as originally formulated, depends only on the net charge that traverses them. Recently, an unambiguous test has been proposed [J. Phys. D: Appl. Phys. {\bf 52}, 01LT01 (2019)] to determine whether a given physical system is indeed a memristor or not. Here, we

  • Mobility-aware Beam Steering in Metasurface-based Programmable Wireless Environments
    arXiv.cs.ET Pub Date : 2020-03-23
    Christos Liaskos; Shuai Nie; Ageliki Tsioliaridou; Andreas Pitsillides; Sotiris Ioannidis; Ian Akyildiz

    Programmable wireless environments (PWEs) utilize electromagnetic metasurfaces to transform wireless propagation into a software-controlled resource. In this work we study the effects of user device mobility on the efficiency of PWEs. An analytical model is proposed, which describes the potential misalignment between user-emitted waves and the active PWE configuration, and can constitute the basis

  • Channel Characterization for 1D Molecular Communication with Two Absorbing Receivers
    arXiv.cs.ET Pub Date : 2019-10-31
    Xinyu Huang; Yuting Fang; Adam Noel; Nan Yang

    This letter develops a one-dimensional (1D) diffusion-based molecular communication system to analyze channel responses between a single transmitter (TX) and two fully-absorbing receivers (RXs). Incorporating molecular degradation in the environment, rigorous analytical formulas for i) the fraction of molecules absorbed, ii) the corresponding hitting rate, and iii) the asymptotic fraction of absorbed

  • Computational universality of fungal sandpile automata
    arXiv.cs.ET Pub Date : 2020-03-19
    Eric Goles; Michail-Antisthenis Tsompanas; Andrew Adamatzky; Martin Tegelaar; Han A. B. Wosten; Genaro J. Martinez

    Hyphae within the mycelia of the ascomycetous fungi are compartmentalised by septa. Each septum has a pore that allows for inter-compartmental and inter-hyphal streaming of cytosol and even organelles. The compartments, however, have special organelles, Woronin bodies, that can plug the pores. When the pores are blocked, no flow of cytoplasm takes place. Inspired by the controllable compartmentalisation

  • Automatic accuracy management of quantum programs via (near-)symbolic resource estimation
    arXiv.cs.ET Pub Date : 2020-03-18
    Giulia Meuli; Mathias Soeken; Martin Roetteler; Thomas Häner

    When compiling programs for fault-tolerant quantum computers, approximation errors must be taken into account. We propose a methodology that tracks such errors automatically and solves the optimization problem of finding accuracy parameters that guarantee a specified overall accuracy while aiming to minimize a custom implementation cost. The core idea is to extract constraint and cost functions directly

  • Quantum Algorithm Implementations for Beginners
    arXiv.cs.ET Pub Date : 2018-04-10
    Abhijith J.; Adetokunbo Adedoyin; John Ambrosiano; Petr Anisimov; Andreas Bärtschi; William Casper; Gopinath Chennupati; Carleton Coffrin; Hristo Djidjev; David Gunter; Satish Karra; Nathan Lemons; Shizeng Lin; Alexander Malyzhenkov; David Mascarenas; Susan Mniszewski; Balu Nadiga; Daniel O'Malley; Diane Oyen; Scott Pakin; Lakshman Prasad; Randy Roberts; Phillip Romero; Nandakishore Santhi; Nikolai

    As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers. While currently available quantum computers have less than 100 qubits, quantum computing hardware is widely expected to grow in terms of qubit count, quality, and connectivity. This review

  • Fungal Automata
    arXiv.cs.ET Pub Date : 2020-03-18
    Andrew Adamatzky; Eric Goles; Genaro J. Martinez; Michail-Antisthenis Tsompanas; Martin Tegelaar; Han A. B. Wosten

    We study a cellular automaton (CA) model of information dynamics on a single hypha of a fungal mycelium. Such a filament is divided in compartments (here also called cells) by septa. These septa are invaginations of the cell wall and their pores allow for flow of cytoplasm between compartments and hyphae. The septal pores of the fungal phylum of the Ascomycota can be closed by organelles called Woronin

  • Traffic Signal Optimization on a Square Lattice using the D-Wave Quantum Annealer
    arXiv.cs.ET Pub Date : 2020-03-17
    Daisuke Inoue; Akihisa Okada; Tadayoshi Matsumori; Kazuyuki Aihara; Hiroaki Yoshida

    The spread of intelligent transportation systems in urban cities has caused heavy computational loads, requiring a novel architecture for managing large-scale traffic. In this study, we develop a method for globally controlling traffic signals arranged on a square lattice by means of a quantum annealing machine, namely the D-Wave quantum annealer. We first formulate a signal optimization problem that

  • Capacitive storage in mycelium substrate
    arXiv.cs.ET Pub Date : 2020-03-17
    Alexander E. Beasley; Anna L. Powell; Andrew Adamatzky

    The emerging field of living technologies aims to create new functional hybrid materials in which living systems interface with artificial ones. Combining research into living technologies with emerging developments in computing architecture has enabled the generation of organic electronics from plants and slime mould. Here, we expand on this work by studying capacitive properties of a substrate colonised

  • Fungal photosensors
    arXiv.cs.ET Pub Date : 2020-03-17
    Alexander E. Beasley; Anna L. Powell; Andrew Adamatzky

    The rapidly developing research field of organic analogue sensors aims to replace traditional semiconductors with naturally occurring materials. Photosensors, or photodetectors, change their electrical properties in response to the light levels they are exposed to. Organic photosensors can be functionalised to respond to specific wavelengths, from ultra-violet to red light. Performing cyclic voltammetry

  • SIMBA: A Skyrmionic In-Memory Binary Neural Network Accelerator
    arXiv.cs.ET Pub Date : 2020-03-11
    Venkata Pavan Kumar Miriyala; Kale Rahul Vishwanath; Xuanyao Fong

    Magnetic skyrmions are emerging as potential candidates for next generation non-volatile memories. In this paper, we propose an in-memory binary neural network (BNN) accelerator based on the non-volatile skyrmionic memory, which we call as SIMBA. SIMBA consumes 26.7 mJ of energy and 2.7 ms of latency when running an inference on a VGG-like BNN. Furthermore, we demonstrate improvements in the performance

  • Enhancing a Near-Term Quantum Accelerator's Instruction Set Architecture for Materials Science Applications
    arXiv.cs.ET Pub Date : 2020-03-06
    Xiang Zou; Shavindra P. Premaratne; M. Adriaan Rol; Sonika Johri; Viacheslav Ostroukh; David J. Michalak; Roman Caudillo; James S. Clarke; Leonardo Dicarlo; A. Y. Matsuura

    Quantum computers with tens to hundreds of noisy qubits are being developed today. To be useful for real-world applications, we believe that these near-term systems cannot simply be scaled-down non-error-corrected versions of future fault-tolerant large-scale quantum computers. These near-term systems require specific architecture and design attributes to realize their full potential. To efficiently

  • Energy-efficient stochastic computing with superparamagnetic tunnel junctions
    arXiv.cs.ET Pub Date : 2019-11-25
    Matthew W. Daniels; Advait Madhavan; Philippe Talatchian; Alice Mizrahi; Mark D. Stiles

    Superparamagnetic tunnel junctions (SMTJs) have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. One of their applications is to generate random bitstreams suitable for use in stochastic computing implementations. We describe a method for digitally programmable bitstream generation based on pre-charge sense amplifiers

  • Deep Learning in Memristive Nanowire Networks
    arXiv.cs.ET Pub Date : 2020-03-03
    Jack D. Kendall; Ross D. Pantone; Juan C. Nino

    Analog crossbar architectures for accelerating neural network training and inference have made tremendous progress over the past several years. These architectures are ideal for dense layers with fewer than roughly a thousand neurons. However, for large sparse layers, crossbar architectures are highly inefficient. A new hardware architecture, dubbed the MN3 (Memristive Nanowire Neural Network), was

  • Laser Induced Speckle as a Foundation for Physical Security and Optical Computing
    arXiv.cs.ET Pub Date : 2020-03-03
    Charis MesaritakisDept. Information & Communication Systems Engineering University of the Aegean Karlovassi-Samos, Greece; Marialena AkriotouDept. Informatics & Telecommunications National & Kapodistrian University of Athens, Athens, Greece; Dimitris SyvridisDept. Informatics & Telecommunications National & Kapodistrian University of Athens, Athens, Greece

    We present a photonic system that exploits the speckle generated by the interaction of a laser source and a semitransparent scattering medium, in our case a large-core optical fiber, as a physical root of trust for cryptographic applications, while the same configuration can act as a high-rate machine learning paradigm.

  • Hardware Design for Autonomous Bayesian Networks
    arXiv.cs.ET Pub Date : 2020-03-02
    Rafatul Faria; Jan Kaiser; Kerem Y. Camsari; Supriyo Datta

    Directed acyclic graphs or Bayesian networks that are popular in many AI related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits built out of probabilistic bits (p-bits), analogous to binary stochastic neurons of stochastic artificial neural networks. In order to satisfy standard statistical results, individual p-bits not only need to be updated sequentially

  • Single photonic perceptron based on a soliton crystal Kerr microcomb for high-speed, scalable, optical neural networks
    arXiv.cs.ET Pub Date : 2020-03-03
    Xingyuan Xu; Mengxi Tan; Bill Corcoran; Jiayang Wu; Thach G. Nguyen; Andreas Boes; Sai T. Chu; Brent E. Little; Roberto Morandotti; Arnan Mitchell; Damien G. Hicks; David J. Moss

    Optical artificial neural networks (ONNs), analog computing hardware tailored for machine learning, have significant potential for ultra-high computing speed and energy efficiency. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building

  • A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision
    arXiv.cs.ET Pub Date : 2020-03-02
    Hongjie Wang; Yang Zhao; Chaojian Li; Yue Wang; Yingyan Lin

    The excellent performance of modern deep neural networks (DNNs) comes at an often prohibitive training cost, limiting the rapid development of DNN innovations and raising various environmental concerns. To reduce the dominant data movement cost of training, process in-memory (PIM) has emerged as a promising solution as it alleviates the need to access DNN weights. However, state-of-the-art PIM DNN

  • Implementation of Optical Deep Neural Networks using the Fabry-Perot Interferometer
    arXiv.cs.ET Pub Date : 2019-11-22
    Benjamin D. Steel

    Future developments in deep learning applications requiring large datasets will be limited by power and speed limitations of silicon based Von-Neumann computing architectures. Optical architectures provide a low power and high speed hardware alternative. Recent publications have suggested promising implementations of optical neural networks (ONNs), showing huge orders of magnitude efficiency and speed

  • Is my Neural Network Neuromorphic? Taxonomy, Recent Trends and Future Directions in Neuromorphic Engineering
    arXiv.cs.ET Pub Date : 2020-02-27
    Sumon Kumar Bose; Jyotibdha Acharya; Arindam Basu

    In this paper, we review recent work published over the last 3 years under the umbrella of Neuromorphic engineering to analyze what are the common features among such systems. We see that there is no clear consensus but each system has one or more of the following features:(1) Analog computing (2) Non vonNeumann Architecture and low-precision digital processing (3) Spiking Neural Networks (SNN) with

  • sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network with On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge
    arXiv.cs.ET Pub Date : 2020-02-25
    Minsuk Koo; Gopalakrishnan Srinivasan; Yong Shim; Kaushik Roy

    In this work, we propose stochastic Binary Spiking Neural Network (sBSNN) composed of stochastic spiking neurons and binary synapses (stochastic only during training) that computes probabilistically with one-bit precision for power-efficient and memory-compressed neuromorphic computing. We present an energy-efficient implementation of the proposed sBSNN using 'stochastic bit' as the core computational

  • Synchronous Counter Design Using Novel Level Sensitive T-FF in QCA Technology
    arXiv.cs.ET Pub Date : 2020-02-04
    Ali H. Majeed; Esam Alkaldy; Mohd Shamian bin Zainal; andDanial Bin MD Nor

    The quantum-dot cellular automata (QCA) nano-technique has attracted computer scientists due to its noticeable features such as low power consumption and small size. Many papers have been published in the literature about the utilization of this technology for de-signing many QCA circuits and for presenting logic gates in an optimal structure. The T flip-flop, which is an essential part of digital

  • QEML (Quantum Enhanced Machine Learning): Using Quantum Computing to Enhance ML Classifiers and Feature Spaces
    arXiv.cs.ET Pub Date : 2020-02-22
    Siddharth Sharma

    Machine learning and quantum computing are two technologies that are causing a paradigm shift in the performance and behavior of certain algorithms, achieving previously unattainable results. Machine learning (kernel classification) has become ubiquitous as the forefront method for pattern recognition and has been shown to have numerous societal applications. While not yet fault-tolerant, Quantum computing

  • Efficient Quantum Circuit Decompositions via Intermediate Qudits
    arXiv.cs.ET Pub Date : 2020-02-24
    Jonathan M. Baker; Casey Duckering; Frederic T. Chong

    Many quantum algorithms make use of ancilla, additional qubits used to store temporary information during computation, to reduce the total execution time. Quantum computers will be resource-constrained for years to come so reducing ancilla requirements is crucial. In this work, we give a method to generate ancilla out of idle qubits by placing some in higher-value states, called qudits. We show how

  • Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks
    arXiv.cs.ET Pub Date : 2020-02-25
    Wen Ma; Pi-Feng Chiu; Won Ho Choi; Minghai Qin; Daniel Bedau; Martin Lueker-Boden

    In cloud and edge computing models, it is important that compute devices at the edge be as power efficient as possible. Long short-term memory (LSTM) neural networks have been widely used for natural language processing, time series prediction and many other sequential data tasks. Thus, for these applications there is increasing need for low-power accelerators for LSTM model inference at the edge.

  • Planning for Compilation of a Quantum Algorithm for Graph Coloring
    arXiv.cs.ET Pub Date : 2020-02-23
    Minh Do; Zhihui Wang; Bryan O'Gorman; Davide Venturelli; Eleanor Rieffel; Jeremy Frank

    The problem of compiling general quantum algorithms for implementation on near-term quantum processors has been introduced to the AI community. Previous work demonstrated that temporal planning is an attractive approach for part of this compilationtask, specifically, the routing of circuits that implement the Quantum Alternating Operator Ansatz (QAOA) applied to the MaxCut problem on a quantum processor

  • Probabilistic Circuits for Autonomous Learning: A simulation study
    arXiv.cs.ET Pub Date : 2019-10-14
    Jan Kaiser; Rafatul Faria; Kerem Y. Camsari; Supriyo Datta

    Modern machine learning is based on powerful algorithms running on digital computing platforms and there is great interest in accelerating the learning process and making it more energy efficient. In this paper we present a fully autonomous probabilistic circuit for fast and efficient learning that makes no use of digital computing. Specifically we use SPICE simulations to demonstrate a clockless autonomous

  • On Boolean gates in fungal colony
    arXiv.cs.ET Pub Date : 2020-02-22
    Andrew Adamatzky; Martin Tegelaar; Han A. B. Wosten; Anna L. Powell; Alexander E. Beasley; Richard Mayne

    A fungal colony maintains its integrity via flow of cytoplasm along mycelium network. This flow, together with possible coordination of mycelium tips propagation, is controlled by calcium waves and associated waves of electrical potential changes. We propose that these excitation waves can be employed to implement a computation in the mycelium networks. We use FitzHugh-Nagumo model to imitate propagation

  • Towards field-programmable photonic gate arrays
    arXiv.cs.ET Pub Date : 2020-02-22
    D. Perez-Lopez; A. López-Hernandez; A. Macho; P. Das Mahapatra; J. Capmany

    We review some of the basic principles, fundamentals, technologies, architectures and recent advances leading to thefor the implementation of Field Programmable Photonic Field Arrays (FPPGAs).

  • Constant Depth Bucket Brigade Quantum RAM Circuits Without Introducing Ancillae
    arXiv.cs.ET Pub Date : 2020-02-21
    Alexandru Paler; Oumarou Oumarou; Robert Basmadjian

    Bucket brigade quantum RAM (QRAM) circuits were proposed for their advantageous addressing of the memory. Another quality of these circuits is that queries, once the addresses are determined, can be parallelised. State-of-the-art error-corrected formulation of these circuits, however, had to be decomposed into the Clifford+T gate set, and the initial parallelism was lost in the process. By using advantageous

  • A Survey of Biological Building Blocks for Synthetic Molecular Communication Systems
    arXiv.cs.ET Pub Date : 2019-01-08
    Christian A. Söldner; Eileen Socher; Vahid Jamali; Wayan Wicke; Arman Ahmadzadeh; Hans-Georg Breitinger; Andreas Burkovski; Kathrin Castiglione; Robert Schober; Heinrich Sticht

    Synthetic molecular communication (MC) is a new communication engineering paradigm which is expected to enable revolutionary applications such as smart drug delivery and real-time health monitoring. The design and implementation of synthetic MC systems (MCSs) at nano- and microscale is very challenging. This is particularly true for synthetic MCSs employing biological components as transmitters and

  • On the Complexity of the Cycles based Synthesis of Ternary Reversible Circuits
    arXiv.cs.ET Pub Date : 2020-02-18
    Caroline BarbieriInstituto Federal de Educação, Ciência e Tecnologia de São Paulo, Brasil; Claudio MoragaTechnical University of Dortmund, Germany

    The paper studies the main aspects of the realization of 2 x 2 ternary reversible circuits based on cycles, considering the results of the realization of all 362,880 2 x 2 ternary reversible functions. It has been shown that in most cases, realizations obtained with the MMD+ algorithm have a lower complexity (in terms of cost) than realizations based on cycles. In the paper it is shown under which

  • A Depth-Aware Swap Insertion Scheme for the Qubit Mapping Problem
    arXiv.cs.ET Pub Date : 2020-02-17
    Chi Zhang; Yanhao Chen; Yuwei Jin; Wonsun Ahn; Youtao Zhang; Eddy Z. Zhang

    The rapid progress of physical implementation of quantum computers paved the way of realising the design of tools to help users write quantum programs for any given quantum devices. The physical constraints inherent to the current NISQ architectures prevent most quantum algorithms from being directly executed on quantum devices. To enable two-qubit gates in the algorithm, existing works focus on inserting

  • Quantum Coupon Collector
    arXiv.cs.ET Pub Date : 2020-02-18
    Srinivasan Arunachalam; Aleksandrs Belovs; Andrew M. Childs; Robin Kothari; Ansis Rosmanis; Ronald de Wolf

    We study how efficiently a $k$-element set $S\subseteq[n]$ can be learned from a uniform superposition $|S\rangle$ of its elements. One can think of $|S\rangle=\sum_{i\in S}|i\rangle/\sqrt{|S|}$ as the quantum version of a uniformly random sample over $S$, as in the classical analysis of the ``coupon collector problem.'' We show that if $k$ is close to $n$, then we can learn $S$ using asymptotically

  • In-materio neuromimetic devices: Dynamics, information processing and pattern recognition
    arXiv.cs.ET Pub Date : 2020-02-14
    Dawid Przyczyna; Piotr Zawal; Tomasz Mazur; Pier Luigi Gentili; Konrad Szaciłowski

    The story of information processing is a story of great success. Todays' microprocessors are devices of unprecedented complexity and MOSFET transistors are considered as the most widely produced artifact in the history of mankind. The current miniaturization of electronic circuits is pushed almost to the physical limit and begins to suffer from various parasitic effects. These facts stimulate intense

  • Quantum computing cryptography: Finding cryptographic Boolean functions with quantum annealing by a 2000 qubit D-wave quantum computer
    arXiv.cs.ET Pub Date : 2018-06-22
    Feng Hu; Lucas Lamata; Mikel Sanz; Xi Chen; Xingyuan Chen; Chao Wang; Enrique Solano

    As the building block in symmetric cryptography, designing Boolean functions satisfying multiple properties is an important problem in sequence ciphers, block ciphers, and hash functions. However, the search of $n$-variable Boolean functions fulfilling global cryptographic constraints is computationally hard due to the super-exponential size $\mathcal{O}(2^{2^n})$ of the space. Here, we introduce a

  • Memristive oscillatory circuits for resolution of NP-complete logic puzzles: Sudoku case
    arXiv.cs.ET Pub Date : 2020-02-15
    Theodoros Panagiotis Chatzinikolaou; Iosif-Angelos Fyrigos; Rafailia-Eleni Karamani; Vasileios Ntinas; Giorgos Dimitrakopoulos; Sorin Cotofana; Georgios Ch. Sirakoulis

    Memristor networks are capable of low-power and massive parallel processing and information storage. Moreover, they have presented the ability to apply for a vast number of intelligent data analysis applications targeting mobile edge devices and low power computing. Beyond the memory and conventional computing architectures, memristors are widely studied in circuits aiming for increased intelligence

  • Unconventional Bio-Inspired Model for Design of Logic Gates
    arXiv.cs.ET Pub Date : 2020-02-13
    Theofanis Floros; Karolos-Alexandros Tsakalos; Nikolaos Dourvas; Michail-Antisthenis Tsompanas; Georgios Ch. Sirakoulis

    During the last years, a well studied biological substrate, namely Physarum polycephalum, has been proven efficient on finding appropriate and efficient solutions in hard to solve complex mathematical problems. The plasmodium of P. polycephalum is a single-cell that serves as a prosperous bio-computational example. Consequently, it has been successfully utilized in the past to solve a variety of path

  • PennyLane: Automatic differentiation of hybrid quantum-classical computations
    arXiv.cs.ET Pub Date : 2018-11-12
    Ville Bergholm; Josh Izaac; Maria Schuld; Christian Gogolin; M. Sohaib Alam; Shahnawaz Ahmed; Juan Miguel Arrazola; Carsten Blank; Alain Delgado; Soran Jahangiri; Keri McKiernan; Johannes Jakob Meyer; Zeyue Niu; Antal Száva; Nathan Killoran

    PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with

  • SLIM: Simultaneous Logic-in-Memory Computing Exploiting Bilayer Analog OxRAM Devices
    arXiv.cs.ET Pub Date : 2018-11-14
    Sandeep Kaur Kingra; Vivek Parmar; Che-Chia Chang; Boris Hudec; Tuo-Hung Hou; Manan Suri

    Von Neumann architecture based computers isolate/physically separate computation and storage units i.e. data is shuttled between computation unit (processor) and memory unit to realize logic/ arithmetic and storage functions. This to-and-fro movement of data leads to a fundamental limitation of modern computers, known as the memory wall. Logic in-Memory (LIM) approaches aim to address this bottleneck

  • Don't take it lightly: Phasing optical random projections with unknown operators
    arXiv.cs.ET Pub Date : 2019-07-03
    Sidharth Gupta; Rémi Gribonval; Laurent Daudet; Ivan Dokmanić

    In this paper we tackle the problem of recovering the phase of complex linear measurements when only magnitude information is available and we control the input. We are motivated by the recent development of dedicated optics-based hardware for rapid random projections which leverages the propagation of light in random media. A signal of interest $\mathbf{\xi} \in \mathbb{R}^N$ is mixed by a random

  • A Scalable Photonic Computer Solving the Subset Sum Problem
    arXiv.cs.ET Pub Date : 2020-02-03
    Xiao-Yun Xu; Xuan-Lun Huang; Zhan-Ming Li; Jun Gao; Zhi-Qiang Jiao; Yao Wang; Ruo-Jing Ren; H. P. Zhang; Xian-Min Jin

    The subset sum problem is a typical NP-complete problem that is hard to solve efficiently in time due to the intrinsic superpolynomial-scaling property. Increasing the problem size results in a vast amount of time consuming in conventionally available computers. Photons possess the unique features of extremely high propagation speed, weak interaction with environment and low detectable energy level

  • Optimal Error Correcting Code For Ternary Quantum Systems
    arXiv.cs.ET Pub Date : 2019-06-26
    Ritajit Majumdar; Susmita Sur-Kolay

    Multi-valued quantum systems can store more information than binary ones for a given number of quantum states. For reliable operation of multi-valued quantum systems, error correction is mandated. In this paper, we propose a 5-qutrit quantum error-correcting code and provide its stabilizer formulation. Since 5 qutrits are necessary to correct a single error, our proposed code is optimal in the number

  • Hardware and software co-optimization for the initialization failure of the ReRAM based cross-bar array
    arXiv.cs.ET Pub Date : 2020-02-11
    Youngseok Kim; Seyoung Kim; Chun-chen Yeh; Vijay Narayanan; Jungwook Choi

    Recent advances in deep neural network demand more than millions of parameters to handle and mandate the high-performance computing resources with improved efficiency. The cross-bar array architecture has been considered as one of the promising deep learning architectures that shows a significant computing gain over the conventional processors. To investigate the feasibility of the architecture, we

  • Control of criticality and computation in spiking neuromorphic networks with plasticity
    arXiv.cs.ET Pub Date : 2019-09-17
    Benjamin Cramer; David Stöckel; Markus Kreft; Michael Wibral; Johannes Schemmel; Karlheinz Meier; Viola Priesemann

    The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying complexity at - and away from critical network dynamics. To that end, we developed a spiking network with

  • The Investigation of Negative Capacitance Vertical Nanowire FETs Based on SPICE Model at Device-Circuit Level
    arXiv.cs.ET Pub Date : 2020-01-18
    Weixing Huang; Huilong Zhu; Kunpeng Jia; Zhenhua Wu; Xiaogen Yin; Qiang Huo; Yongkui Zhang

    In this study, a SPICE model for negative capacitance vertical nanowire field-effect-transistor (NC VNW-FET) based on BSIM-CMG model and Landau-Khalatnikov (LK) equation was presented. Suffering from the limitation of short gate length there is lack of controllable and integrative structures for high performance NC VNW-FETs. A new kind of structure was proposed for NC VNW-FETs at sub-3nm node. Moreover

  • Nonparametric Regression Quantum Neural Networks
    arXiv.cs.ET Pub Date : 2020-02-07
    Do Ngoc Diep; Koji Nagata; Tadao Nakamura

    In two pervious papers \cite{dndiep3}, \cite{dndiep4}, the first author constructed the least square quantum neural networks (LS-QNN), and ploynomial interpolation quantum neural networks ( PI-QNN), parametrico-stattistical QNN like: leanr regrassion quantum neural networks (LR-QNN), polynomial regression quantum neural networks (PR-QNN), chi-squared quantum neural netowrks ($\chi^2$-QNN). We observed

  • Hybrid Pass Transistor Logic with Dual-Gate Ambipolar CNTFETs
    arXiv.cs.ET Pub Date : 2020-02-05
    Xuan Hu; Joseph S. Friedman

    Pass Transistor Logic (PTL) provides decreased device count relative to conventional complementary circuit structures, but the buffering inverters required for cascading PTL gates diminishes this advantage. Dual-gate ambipolar carbon nanotube transistors (DG-A-CNTFETs) are a natural match for PTL due to the fact that both require significant use of inverters between logic stages. This work therefore

  • Optimum multiplexer design in quantum-dot cellular automata
    arXiv.cs.ET Pub Date : 2020-02-02
    Esam Alkaldy; Ali H. Majeed; Mohd Shamian Zainal; Danial Md. Nor

    Quantum-dot Cellular Automata (QCA) is one of the most important computing technologies for the future and will be the alternative candidate for current CMOS technology. QCA is attracting a lot of researchers due to many features such as high speed, small size, and low power consumption. QCA has two main building blocks (majority gate and inverter) used for design any Boolean function. QCA also has

  • Parallel convolution processing using an integrated photonic tensor core
    arXiv.cs.ET Pub Date : 2020-02-01
    Johannes Feldmann; Nathan Youngblood; Maxim Karpov; Helge Gehring; Xuan Li; Manuel Le Gallo; Xin Fu; Anton Lukashchuk; Arslan Raja; Junqiu Liu; David Wright; Abu Sebastian; Tobias Kippenberg; Wolfram Pernice; Harish Bhaskaran

    With the proliferation of ultra-high-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence, the world is generating exponentially increasing amounts of data-data that needs to be processed in a fast, efficient and 'smart' way. These developments are pushing the limits of existing computing paradigms, and highly parallelized, fast and scalable hardware

  • CMOS-Free Multilayer Perceptron Enabled by Four-Terminal MTJ Device
    arXiv.cs.ET Pub Date : 2020-02-03
    Wesley H. Brigner; Naimul Hassan; Xuan Hu; Christopher H. Bennett; Felipe Garcia-Sanchez; Matthew J. Marinella; Jean Anne C. Incorvia; Joseph S. Friedman

    Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information. To fully realize this potential, neuromorphic systems should exploit the biomimetic behavior of emerging nanodevices. In particular, exceptional opportunities are provided by the non-volatility and analog capabilities of spintronic devices. While spintronic devices

  • Modular Simulation Framework for Process Variation Analysis of MRAM-based Deep Belief Networks
    arXiv.cs.ET Pub Date : 2020-02-03
    Paul Wood; Hossein Pourmeidani; Ronald F. DeMara

    Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing devices are garnering increasing interest as a means to compactly and efficiently realize machine learning operations in Restricted Boltzmann Machines (RBMs). When embedded within an RBM resistive crossbar array, the p-bit based neuron realizes a tunable sigmoidal activation function. Since the stochasticity of activation is dependent

  • Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
    arXiv.cs.ET Pub Date : 2020-02-03
    Sebastian Buschjäger; Jian-Jia Chen; Kuan-Hsun Chen; Mario Günzel; Christian Hakert; Katharina Morik; Rodion Novkin; Lukas Pfahler; Mikail Yayla

    Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, that the bit error rate of RRAMs can be up to 3.3% in the ultra low-power setting, which might be crucial for many use cases. Binary neural networks (BNNs), a resource efficient variant of neural networks (NNs), can tolerate

  • Mixed-precision deep learning based on computational memory
    arXiv.cs.ET Pub Date : 2020-01-31
    S. R. Nandakumar; Manuel Le Gallo; Christophe Piveteau; Vinay Joshi; Giovanni Mariani; Irem Boybat; Geethan Karunaratne; Riduan Khaddam-Aljameh; Urs Egger; Anastasios Petropoulos; Theodore Antonakopoulos; Bipin Rajendran; Abu Sebastian; Evangelos Eleftheriou

    Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices

  • Statistical Tests and Confidential Intervals as Thresholds for Quantum Neural Networks
    arXiv.cs.ET Pub Date : 2020-01-30
    Do Ngoc Diep

    Some basic quantum neural networks were analyzed and constructed in the recent work of the author \cite{dndiep3}, published in International Journal of Theoretical Physics (2020). In particular the Least Quare Problem (LSP) and the Linear Regression Problem (LRP) was discussed. In this second paper we continue to analyze and construct the least square quantum neural network (LS-QNN), the polynomial

  • A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning
    arXiv.cs.ET Pub Date : 2020-01-31
    Jennifer Sleeman; John Dorband; Milton Halem

    Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work evaluates the feasibility of using the D-Wave as a sampler for machine learning. We describe a hybrid system that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave. We evaluate our hybrid autoencoder

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