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  • Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics
    arXiv.cs.ET Pub Date : 2020-07-06
    Samiran Ganguly; Avik W. Ghosh

    Biologically inspired recurrent neural networks, such as reservoir computers are of interest in designing spatio-temporal data processors from a hardware point of view due to the simple learning scheme and deep connections to Kalman filters. In this work we discuss using in-depth simulation studies a way to construct hardware reservoir computers using an analog stochastic neuron cell built from a low

    更新日期:2020-07-07
  • Novel Memory Structures in QCA Nano Technology
    arXiv.cs.ET Pub Date : 2020-07-03
    Ali H. Majeed; Esam Alkaldy; Mohd S. Zainal; Danial MD. Nor

    Quantum-dot Cellular Automata (QCA) is a new emerging technology for designing electronic circuits in nanoscale. QCA technology comes to overcome the CMOS limitation and to be a good alternative as it can work in ultra-high-speed. QCA brought researchers attention due to many features such as low power consumption, small feature size in addition to high frequency. Designing circuits in QCA technology

    更新日期:2020-07-07
  • Transfer Function Models for Cylindrical MC Channels with Diffusion and Laminar Flow
    arXiv.cs.ET Pub Date : 2020-07-03
    Maximilian Schäfer; Wayan Wicke; Lukas Brand; Rudolf Rabenstein; Robert Schober

    The analysis and design of advection-diffusion based molecular communication (MC) systems in cylindrical environments is of particular interest for applications such as micro-fluidics and targeted drug delivery in blood vessels. Therefore, the accurate modeling of the corresponding MC channel is of high importance. The propagation of particles in these systems is caused by a combination of diffusion

    更新日期:2020-07-06
  • Threshold Logic with Current-Driven Magnetic Domain Walls
    arXiv.cs.ET Pub Date : 2020-07-02
    Xuan Hu; Brighton A. Hill; Felipe Garcia-Sanchez; Joseph S. Friedman

    The recent demonstration of current-driven magnetic domain wall logic [2] was based on a three-input logic gate that was identified as a reconfigurable NAND/NOR function. We reinterpret this logic gate as a minority gate within the context of threshold logic, enabling a domain wall threshold logic paradigm in which the device count can be reduced by 80%. Furthermore, by extending the logic gate to

    更新日期:2020-07-03
  • Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons
    arXiv.cs.ET Pub Date : 2020-07-01
    Umang Garg; Kezhou Yang; Abhronil Sengupta

    Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain. In this letter, we demonstrate that injected radio-frequency signal in underlying heavy metal layer of spin-orbit torque oscillator neurons mimic the neuron phase synchronization effect realized by glial cells. Potential application of such phase coupling effects is illustrated in the context

    更新日期:2020-07-03
  • Realizing Quantum Algorithms on Real Quantum Computing Devices
    arXiv.cs.ET Pub Date : 2020-07-02
    Carmen G. Almudever; Lingling Lao; Robert Wille; Gian Giacomo Guerreschi

    Quantum computing is currently moving from an academic idea to a practical reality. Quantum computing in the cloud is already available and allows users from all over the world to develop and execute real quantum algorithms. However, companies which are heavily investing in this new technology such as Google, IBM, Rigetti, Intel, IonQ, and Xanadu follow diverse technological approaches. This led to

    更新日期:2020-07-03
  • TDO-CIM: Transparent Detection and Offloading for Computation In-memory
    arXiv.cs.ET Pub Date : 2020-06-30
    Kanishkan Vadivel; Lorenzo Chelini; Ali BanaGozar; Gagandeep Singh; Stefano Corda; Roel Jordans; Henk Corporaal

    Computation in-memory is a promising non-von Neumann approach aiming at completely diminishing the data transfer to and from the memory subsystem. Although a lot of architectures have been proposed, compiler support for such architectures is still lagging behind. In this paper, we close this gap by proposing an end-to-end compilation flow for in-memory computing based on the LLVM compiler infrastructure

    更新日期:2020-07-02
  • Towards analyzing large graphs with quantum annealing and quantum gate computers
    arXiv.cs.ET Pub Date : 2020-06-30
    Hannu Reittu; Ville Kotovirta; Lasse Leskelä; Hannu Rummukainen; Tomi Räty

    The use of quantum computing in graph community detection and regularity checking related to Szemeredi's Regularity Lemma (SRL) are demonstrated with D-Wave Systems' quantum annealer and simulations. We demonstrate the capability of quantum computing in solving hard problems relevant to big data. A new community detection algorithm based on SRL is also introduced and tested. In worst case scenario

    更新日期:2020-07-01
  • A Compact Gated-Synapse Model for Neuromorphic Circuits
    arXiv.cs.ET Pub Date : 2020-06-29
    Alexander Jones; Rashmi Jha

    This work reports a compact behavioral model for gated-synaptic memory. The model is developed in Verilog-A for easy integration into computer-aided design of neuromorphic circuits using emerging memory. The model encompasses various forms of gated synapses within a single framework and is not restricted to only a single type. The behavioral theory of the model is described in detail along with a full

    更新日期:2020-07-01
  • Mutual Information in Coupled Double Quantum Dots: A Simple Analytic Model for Potential Artificial Consciousness
    arXiv.cs.ET Pub Date : 2020-06-27
    Katsuaki Tanabe

    The integrated information theory is thought to be a key clue towards the theoretical understanding of consciousness. In this study, we propose a simple numerical model comprising a set of coupled double quantum dots, where the disconnection of the elements is represented by the removal of Coulomb interaction between the quantum dots, for the quantitative investigation of integrated information. As

    更新日期:2020-07-01
  • Graphene-based Nanoscale Molecular Communication Receiver: Fabrication and Microfluidic Analysis
    arXiv.cs.ET Pub Date : 2020-06-28
    Murat Kuscu; Hamideh Ramezani; Ergin Dinc; Shahab Akhavan; Ozgur B. Akan

    Bio-inspired molecular communications (MC), where molecules are used to transfer information, is the most promising technique to realise the Internet of Nano Things (IoNT), thanks to its inherent biocompatibility, energy-efficiency, and reliability in physiologically-relevant environments. Despite a substantial body of theoretical work concerning MC, the lack of practical micro/nanoscale MC devices

    更新日期:2020-06-30
  • Quantum Approximate Optimization for Hard Problems in Linear Algebra
    arXiv.cs.ET Pub Date : 2020-06-27
    Ajinkya Borle; Vincent E. Elfving; Samuel J. Lomonaco

    The Quantum Approximate Optimization Algorithm (QAOA) by Farhi et al. is a framework for hybrid quantum/classical optimization. In this paper, we explore using QAOA for binary linear least squares; a problem that can serve as a building block of several other hard problems in linear algebra. Most of the previous efforts in quantum computing for solving these problems were done using the quantum annealing

    更新日期:2020-06-30
  • Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence
    arXiv.cs.ET Pub Date : 2020-06-25
    Arianna Rubino; Can Livanelioglu; Ning Qiao; Melika Payvand; Giacomo Indiveri

    Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism

    更新日期:2020-06-26
  • Developing of a photonic hardware platform for brain-inspired computing based on $5\times5$ VCSEL arrays
    arXiv.cs.ET Pub Date : 2020-06-24
    T. Heuser; M. Pflüger; I. Fischer; J. A. Lott; D. Brunner; S. Reitzenstein

    Brain-inspired computing concepts like artificial neural networks have become promising alternatives to classical von Neumann computer architectures. Photonic neural networks target the realizations of neurons, network connections and potentially learning in photonic substrates. Here, we report the development of a nanophotonic hardware platform of fast and energy-efficient photonic neurons via arrays

    更新日期:2020-06-26
  • Quantum Shuttle: Traffic Navigation with Quantum Computing
    arXiv.cs.ET Pub Date : 2020-06-23
    Sheir Yarkoni; Florian Neukart; Eliane Moreno Gomez Tagle; Nicole Magiera; Bharat Mehta; Kunal Hire; Swapnil Narkhede; Martin Hofmann

    The Web Summit conference in Lisbon, Portugal, is one of the biggest technology conferences in Europe, attended by tens of thousands of people every year. The high influx of people into Lisbon causes significant stress on the city's transit services for the duration of the conference. For the Web Summit 2019, Volkswagen AG partnered with the city of Lisbon for a pilot project to provide quantum computing-based

    更新日期:2020-06-26
  • Freely scalable and reconfigurable optical hardware for deep learning
    arXiv.cs.ET Pub Date : 2020-06-24
    Liane BernsteinMassachusetts Institute of Technology; Alexander SluddsMassachusetts Institute of Technology; Ryan HamerlyMassachusetts Institute of TechnologyNTT Research Inc; Vivienne SzeMassachusetts Institute of Technology; Joel EmerMassachusetts Institute of TechnologyNVIDIA; Dirk EnglundMassachusetts Institute of Technology

    As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural

    更新日期:2020-06-25
  • Quantum Computer: Hello, Music!
    arXiv.cs.ET Pub Date : 2020-06-21
    Eduardo R. Miranda

    Quantum computing is emerging as a promising technology, which is built on the principles of subatomic physics. By the time of writing, fully fledged practical quantum computers are not widely available. But research and development are advancing rapidly. Various software simulators are already available. And a few companies have already started to provide access to quantum hardware via the cloud.

    更新日期:2020-06-25
  • End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction
    arXiv.cs.ET Pub Date : 2020-06-22
    Abdullah M. Zyarah; Kevin Gomez; Dhireesha Kudithipudi

    Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this paper, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on hierarchical temporal memory that inherently offers online learning, resiliency, and fault tolerance. Architecturally

    更新日期:2020-06-23
  • Optimizing Placement of Heap Memory Objects in Energy-Constrained Hybrid Memory Systems
    arXiv.cs.ET Pub Date : 2020-06-22
    Taeuk Kim; Safdar Jamil; Joongeon Park; Youngjae Kim

    Main memory (DRAM) significantly impacts the power and energy utilization of the overall server system. Non-Volatile Memory (NVM) devices, such as Phase Change Memory and Spin-Transfer Torque RAM, are suitable candidates for main memory to reduce energy consumption. But unlike DRAM, NVMs access latencies are higher than DRAM and NVM writes are more energy sensitive than DRAM write operations. Thus

    更新日期:2020-06-23
  • In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications
    arXiv.cs.ET Pub Date : 2020-06-20
    Bogdan Penkovsky; Marc Bocquet; Tifenn Hirtzlin; Jacques-Olivier Klein; Etienne Nowak; Elisa Vianello; Jean-Michel Portal; Damien Querlioz

    The advent of deep learning has considerably accelerated machine learning development. The deployment of deep neural networks at the edge is however limited by their high memory and energy consumption requirements. With new memory technology available, emerging Binarized Neural Networks (BNNs) are promising to reduce the energy impact of the forthcoming machine learning hardware generation, enabling

    更新日期:2020-06-23
  • Colour-Specific Microfluidic Droplet Detection for Molecular Communication
    arXiv.cs.ET Pub Date : 2020-06-17
    Max Bartunik; Marco Fleischer; Werner Haselmayr; Jens Kirchner

    Droplet-based microfluidic systems are a promising platform forlab-on-a-chip (LoC) applications. These systems can also be used toenhance LoC applications with integrated droplet control information or for data transmission scenarios in the context of molecular communication. For both use-cases the detection and characterisation of droplets in small microfluidic channels is crucial. So far, only complex

    更新日期:2020-06-18
  • Quantum simulation and circuit design for solving multidimensional Poisson equations
    arXiv.cs.ET Pub Date : 2020-06-16
    Michael Holzmann; Harald Koestler

    Many methods solve Poisson equations by using grid techniques which discretize the problem in each dimension. Most of these algorithms are subject to the curse of dimensionality, so that they need exponential runtime. In the paper "Quantum algorithm and circuit design solving the Poisson equation" a quantum algorithm is shown running in polylog time to produce a quantum state representing the solution

    更新日期:2020-06-16
  • A Design Methodology for Post-Moore's Law Accelerators: The Case of a Photonic Neuromorphic Processor
    arXiv.cs.ET Pub Date : 2020-06-15
    Armin Mehrabian; Volker J. Sorger; Tarek El-Ghazawi

    Over the past decade alternative technologies have gained momentum as conventional digital electronics continue to approach their limitations, due to the end of Moore's Law and Dennard Scaling. At the same time, we are facing new application challenges such as those due to the enormous increase in data. The attention, has therefore, shifted from homogeneous computing to specialized heterogeneous solutions

    更新日期:2020-06-15
  • Continual General Chunking Problem and SyncMap
    arXiv.cs.ET Pub Date : 2020-06-14
    Danilo Vasconcellos Vargas; Toshitake Asabuki

    Humans possess an inherent ability to chunk sequences into their constituent parts. In fact, this ability is thought to bootstrap language skills to the learning of image patterns which might be a key to a more animal-like type of intelligence. Here, we propose a continual generalization of the chunking problem (an unsupervised problem), encompassing fixed and probabilistic chunks, discovery of temporal

    更新日期:2020-06-14
  • Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate
    arXiv.cs.ET Pub Date : 2020-06-12
    Benjamin Cramer; Sebastian Billaudelle; Simeon Kanya; Aron Leibfried; Andreas Grübl; Vitali Karasenko; Christian Pehle; Korbinian Schreiber; Yannik Stradmann; Johannes Weis; Johannes Schemmel; Friedemann Zenke

    Spiking neural networks are nature's solution for parallel information processing with high temporal precision at a low metabolic energy cost. To that end, biological neurons integrate inputs as an analog sum and communicate their outputs digitally as spikes, i.e., sparse binary events in time. These architectural principles can be mirrored effectively in analog neuromorphic hardware. Nevertheless

    更新日期:2020-06-12
  • Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware
    arXiv.cs.ET Pub Date : 2020-06-11
    Adarsha Balaji; Thibaut Marty; Anup Das; Francky Catthoor

    In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at {run-time}. Our design methodology operates in two steps -- step 1 is a layer-wise greedy approach to partition SNNs into clusters of neurons and synapses incorporating the constraints of the neuromorphic architecture, and step 2 is

    更新日期:2020-06-11
  • Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques
    arXiv.cs.ET Pub Date : 2020-06-11
    Rawan Alghamdi; Reem Alhadrami; Dalia Alhothali; Heba Almorad; Alice Faisal; Sara Helal; Rahaf Shalabi; Rawan Asfour; Noofa Hammad; Asmaa Shams; Nasir Saeed; Hayssam Dahrouj; Tareq Y. Al-Naffouri; Mohamed-Slim Alouini

    This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support next generation wireless physical platforms (6G). Due to their ability to adjust the channels through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency

    更新日期:2020-06-11
  • Loss-of-entanglement prediction of a controlled-PHASE gate in the framework of steepest-entropy-ascent quantum thermodynamics
    arXiv.cs.ET Pub Date : 2020-06-10
    J. A. Montañez-Barrera; Cesar E. Damian-Ascencio; Michael R. von Spakovsky; Sergio Cano-Andrade

    As has been shown elsewhere, a reasonable model of the loss of entanglement or correlation that occurs in quantum computations is one which assumes that they can effectively be predicted by a framework that presupposes the presence of irreversibilities internal to the system. It is based on the steepest-entropy-ascent principle and is used here to reproduce the behavior of a controlled-PHASE gate in

    更新日期:2020-06-10
  • Methodology for Realizing VMM with Binary RRAM Arrays: Experimental Demonstration of Binarized-ADALINE Using OxRAM Crossbar
    arXiv.cs.ET Pub Date : 2020-06-10
    Sandeep Kaur Kingra; Vivek Parmar; Shubham Negi; Sufyan Khan; Boris Hudec; Tuo-Hung Hou; Manan Suri

    In this paper, we present an efficient hardware mapping methodology for realizing vector matrix multiplication (VMM) on resistive memory (RRAM) arrays. Using the proposed VMM computation technique, we experimentally demonstrate a binarized-ADALINE (Adaptive Linear) classifier on an OxRAM crossbar. An 8x8 OxRAM crossbar with Ni/3-nm HfO2/7 nm Al-doped-TiO2/TiN device stack is used. Weight training for

    更新日期:2020-06-10
  • Improving Dependability of Neuromorphic Computing With Non-Volatile Memory
    arXiv.cs.ET Pub Date : 2020-06-10
    Shihao Song; Anup Das; Nagarajan Kandasamy

    As process technology continues to scale aggressively, circuit aging in a neuromorphic hardware due to negative bias temperature instability (NBTI) and time-dependent dielectric breakdown (TDDB) is becoming a critical reliability issue and is expected to proliferate when using non-volatile memory (NVM) for synaptic storage. This is because an NVM requires high voltage and current to access its synaptic

    更新日期:2020-06-10
  • Unified Characterization Platform for Emerging NVM Technology: Neural Network Application Benchmarking Using off-the-shelf NVM Chips
    arXiv.cs.ET Pub Date : 2020-06-10
    Supriya Chakraborty; Abhishek Gupta; Manan Suri

    In this paper, we present a unified FPGA based electrical test-bench for characterizing different emerging NonVolatile Memory (NVM) chips. In particular, we present detailed electrical characterization and benchmarking of multiple commercially available, off-the-shelf, NVM chips viz.: MRAM, FeRAM, CBRAM, and ReRAM. We investigate important NVM parameters such as: (i) current consumption patterns, (ii)

    更新日期:2020-06-10
  • Evaluation of Quantum Approximate Optimization Algorithm based on the approximation ratio of single samples
    arXiv.cs.ET Pub Date : 2020-06-08
    Jason Larkin; Matías Jonsson; Daniel Justice; Gian Giacomo Guerreschi

    The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm to solve binary-variable optimization problems. Due to its expected robustness to systematic errors and the short circuit depth, it is one of the promising candidates likely to run on near-term quantum devices. We project the performance of QAOA applied to the Max-Cut problem and compare it with some of the

    更新日期:2020-06-08
  • Lowering the T-depth of Quantum Circuits By Reducing the Multiplicative Depth Of Logic Networks
    arXiv.cs.ET Pub Date : 2020-06-06
    Thomas Häner; Mathias Soeken

    The multiplicative depth of a logic network over the gate basis $\{\land, \oplus, \neg\}$ is the largest number of $\land$ gates on any path from a primary input to a primary output in the network. We describe a dynamic programming based logic synthesis algorithm to reduce the multiplicative depth in logic networks. It makes use of cut enumeration, tree balancing, and exclusive sum-of-products (ESOP)

    更新日期:2020-06-06
  • HIPE-MAGIC: A Technology-Aware Synthesis and Mapping Flow for HIghly Parallel Execution of Memristor-Aided LoGIC
    arXiv.cs.ET Pub Date : 2020-06-05
    Arash Fayyazi; Amirhossein Esmaili; Massoud Pedram

    Recent efforts for finding novel computing paradigms that meet today's design requirements have given rise to a new trend of processing-in-memory relying on non-volatile memories. In this paper, we present HIPE-MAGIC, a technology-aware synthesis and mapping flow for highly parallel execution of the memristor-based logic. Our framework is built upon two fundamental contributions: balancing techniques

    更新日期:2020-06-05
  • 6RLR-ABC: 6LoWPAN Routing Protocol With Local Repair Using Bio Inspired Artificial Bee Colony
    arXiv.cs.ET Pub Date : 2020-06-05
    Nurul Halimatul Asmak Ismail; Samer A. B. Awwad; Rosilah Hassan

    In recent years, Micro-Electro-Mechanical System (MEMS) has successfully enabled the development of IPv6 over Low power Wireless Personal Area Network (6LoWPAN). This network is equipped with low-cost, low-power, lightweight and varied functions devices. These devices are capable of amassing, storing, processing environmental information and conversing with neighbouring sensors. These requisites pose

    更新日期:2020-06-05
  • Counting Cards: Exploiting Weight and Variance Distributions for Robust Compute In-Memory
    arXiv.cs.ET Pub Date : 2020-06-04
    Brian Crafton; Samuel Spetalnick; Arijit Raychowdhury

    Compute in-memory (CIM) is a promising technique that minimizes data transport, the primary performance bottleneck and energy cost of most data intensive applications. This has found wide-spread adoption in accelerating neural networks for machine learning applications. Utilizing a crossbar architecture with emerging non-volatile memories (eNVM) such as dense resistive random access memory (RRAM) or

    更新日期:2020-06-04
  • An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM)
    arXiv.cs.ET Pub Date : 2020-06-04
    Hüsrev Cılasun; Salonik Resch; Zamshed I. Chowdhury; Erin Olson; Masoud Zabihi; Zhengyang Zhao; Thomas Peterson; Keshab Parhi; Jian-Ping Wang; Sachin S. Sapatnekar; Ulya Karpuzcu

    Spiking Neural Networks (SNN) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise is very low energy consumption. Unfortunately, classic Von Neumann architecture based SNN accelerators often fail to address demanding computation and data transfer requirements efficiently at scale. In this work, we propose

    更新日期:2020-06-04
  • Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map
    arXiv.cs.ET Pub Date : 2020-06-04
    Andrei Velichko

    The study presents a neural network, which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective MNIST handwritten digit recognition

    更新日期:2020-06-04
  • Implementing a Quantum Coin Scheme
    arXiv.cs.ET Pub Date : 2020-06-03
    Hazel Murray; Jerry Horgan; Joao F. Santos; David Malone; Harun Siljak

    Quantum computing has the power to break current cryptographic systems, disrupting online banking, shopping, data storage and communications. Quantum computing also has the power to support stronger more resistant technologies. In this paper, we describe a digital cash scheme created by Dmitry Gavinsky, which utilises the capability of quantum computing. We contribute by setting out the methods for

    更新日期:2020-06-03
  • Hardware Security in Spin-Based Computing-In-Memory: Analysis, Exploits, and Mitigation Techniques
    arXiv.cs.ET Pub Date : 2020-06-02
    Xueyan Wang; Jianlei Yang; Yinglin Zhao; Xiaotao Jia; Gang Qu; Weisheng Zhao

    Computing-in-memory (CIM) is proposed to alleviate the processor-memory data transfer bottleneck in traditional Von-Neumann architectures, and spintronics-based magnetic memory has demonstrated many facilitation in implementing CIM paradigm. Since hardware security has become one of the major concerns in circuit designs, this paper, for the first time, investigates spin-based computing-in-memory (SpinCIM)

    更新日期:2020-06-02
  • SeqXFilter: A Memory-efficient Denoising Filter for Dynamic Vision Sensors
    arXiv.cs.ET Pub Date : 2020-06-02
    Shasha Guo; Lei Wang; Xiaofan Chen; Limeng Zhang; Ziyang Kang; Weixia Xu

    Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imaging sensors. However, they are sensitive to background activity (BA) events that are unwanted. There are some filters for tackling this problem based on spatio-temporal correlation. However, they are either memory-intensive or computing-intensive. We propose \emph{SeqXFilter}

    更新日期:2020-06-02
  • Light-in-the-loop: using a photonics co-processor for scalable training of neural networks
    arXiv.cs.ET Pub Date : 2020-06-02
    Julien Launay; Iacopo Poli; Kilian Müller; Igor Carron; Laurent Daudet; Florent Krzakala; Sylvain Gigan

    As neural networks grow larger and more complex and data-hungry, training costs are skyrocketing. Especially when lifelong learning is necessary, such as in recommender systems or self-driving cars, this might soon become unsustainable. In this study, we present the first optical co-processor able to accelerate the training phase of digitally-implemented neural networks. We rely on direct feedback

    更新日期:2020-06-02
  • SOT-MRAM based Sigmoidal Neuron for Neuromorphic Architectures
    arXiv.cs.ET Pub Date : 2020-06-01
    Brendan Reidy; Ramtin Zand

    In this paper, the intrinsic physical characteristics of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) devices are leveraged to realize sigmoidal neurons in neuromorphic architectures. Performance comparisons with the previous power- and area-efficient sigmoidal neuron circuits exhibit 74x and 12x reduction in power-area-product values for the proposed SOT-MRAM based neuron.

    更新日期:2020-06-01
  • Quantum Accelerated Estimation of Algorithmic Information
    arXiv.cs.ET Pub Date : 2020-06-01
    Aritra Sarkar; Zaid Al-Ars; Koen Bertels

    In this research we present a quantum circuit for estimating algorithmic information metrics like the universal prior distribution. This accelerates inferring algorithmic structure in data for discovering causal generative models. The computation model is restricted in time and space resources to make it computable in approximating the target metrics. A classical exhaustive enumeration is shown for

    更新日期:2020-06-01
  • Scalable spin-glass optical simulator
    arXiv.cs.ET Pub Date : 2020-06-01
    Davide Pierangeli; Mushegh Rafayelyan; Claudio Conti; Sylvain Gigan

    Many developments in science and engineering depend on tackling complex optimizations on large scales. The challenge motivates intense search for specific computing hardware that takes advantage from quantum features [1, 2], stochastic elements [3], nonlinear dissipative dynamics [4-8], in-memory operations [9, 10], or photonics [11-14]. A paradigmatic optimization problem is finding low-energy states

    更新日期:2020-06-01
  • Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models
    arXiv.cs.ET Pub Date : 2020-05-28
    Dharani Punithan; Byoung-Tak Zhang

    We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model. Our PMRF-based molecular associative memory model extracts locally distributed features from the exposed examples, learns and stores the patterns in the molecular associative memory and denoises the given noisy patterns via DNA computation based operations

    更新日期:2020-05-28
  • Logic Verification of Ultra-Deep Pipelined Beyond-CMOS Technologies
    arXiv.cs.ET Pub Date : 2020-05-28
    Arash Fayyazi; Shahin Nazarian; Massoud Pedram

    Traditional logical equivalence checking (LEC) which plays a major role in entire chip design process faces challenges of meeting the requirements demanded by the many emerging technologies that are based on logic models different from standard complementary metal oxide semiconductor (CMOS). In this paper, we propose a LEC framework to be employed in the verification process of beyond-CMOS circuits

    更新日期:2020-05-28
  • 3D logic cells design and results based on Vertical NWFET technology including tied compact model
    arXiv.cs.ET Pub Date : 2020-05-28
    C. Mukherjee; M. Deng; F. Marc; C. Maneux; A. Poittevin; I. OConnor; S. Le Beux; A. Kumar; A. Lecestre; G. Larrieu

    Gate-all-around Vertical Nanowire Field Effect Transistors (VNWFET) are emerging devices, which are well suited to pursue scaling beyond lateral scaling limitations around 7nm. This work explores the relative merits and drawbacks of the technology in the context of logic cell design. We describe a junctionless nanowire technology and associated compact model, which accurately describes fabricated device

    更新日期:2020-05-28
  • Molecular MUX-Based Physical Unclonable Functions
    arXiv.cs.ET Pub Date : 2020-05-27
    Lulu Ge; Keshab K. Parhi

    Physical unclonable functions (PUFs) are small circuits that are widely used as hardware security primitives for authentication. These circuits can generate unique signatures because of the inherent randomness in manufacturing and process variations. This paper introduces molecular PUFs based on multiplexer (MUX) PUFs using dual-rail representation. It may be noted that molecular PUFs have not been

    更新日期:2020-05-27
  • Limitations on counting in Boolean circuits and self-assembly
    arXiv.cs.ET Pub Date : 2020-05-27
    Tristan Stérin; Damien Woods

    In self-assembly, a $k$-counter is a tile set that grows a horizontal ruler from left to right, containing $k$ columns each of which encodes a distinct binary string. Counters have been fundamental objects of study in a wide range of theoretical models of tile assembly, molecular robotics and thermodynamics-based self-assembly due to their construction capabilities using few tile types, time-efficiency

    更新日期:2020-05-27
  • Arm order recognition in multi-armed bandit problem with laser chaos time series
    arXiv.cs.ET Pub Date : 2020-05-26
    Naoki Narisawa; Nicolas Chauvet; Mikio Hasegawa; Makoto Naruse

    By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward expectation, the correct recognition of the order of arms in terms of reward expectations

    更新日期:2020-05-26
  • Just-in-time Quantum Circuit Transpilation Reduces Noise
    arXiv.cs.ET Pub Date : 2020-05-26
    Ellis Wilson; Sudhakar Singh; Frank Mueller

    Running quantum programs is fraught with challenges on on today's noisy intermediate scale quantum (NISQ) devices. Many of these challenges originate from the error characteristics that stem from rapid decoherence and noise during measurement, qubit connections, crosstalk, the qubits themselves, and transformations of qubit state via gates. Not only are qubits not "created equal", but their noise level

    更新日期:2020-05-26
  • A Quantum Annealing Approach for Dynamic Multi-Depot Capacitated Vehicle Routing Problem
    arXiv.cs.ET Pub Date : 2020-05-26
    Ramkumar Harikrishnafkumar; Saideep Nannapaneni; Nam H. Nguyen; James E. Steck; Elizabeth C. Behrman

    Quantum annealing (QA) is a quantum computing algorithm that works on the principle of Adiabatic Quantum Computation (AQC), and it has shown significant computational advantages in solving combinatorial optimization problems such as vehicle routing problems (VRP) when compared to classical algorithms. This paper presents a QA approach for solving a variant VRP known as multi-depot capacitated vehicle

    更新日期:2020-05-26
  • Experimental evaluation of quantum Bayesian networks on IBM QX hardware
    arXiv.cs.ET Pub Date : 2020-05-26
    Sima E. Borujeni; Nam H. Nguyen; Saideep Nannapaneni; Elizabeth C. Behrman; James E. Steck

    Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the

    更新日期:2020-05-26
  • A Physical Channel Model for Wired Nano-Communication Networks
    arXiv.cs.ET Pub Date : 2020-05-25
    Oussama Abderrahmane Dambri; Soumaya Cherkaoui

    In this paper, we propose a new end-to-end system for wired nano-communication networks using a self-assembled polymer. The self-assembly of a polymer creates a channel between the transmitter and the receiver in the form of a conductive nanowire that uses electrons as carriers of information. We derive the channel's analytical model and its master equation to study the dynamic process of the polymer

    更新日期:2020-05-25
  • Quantum Circuits for Functionally Controlled NOT Gates
    arXiv.cs.ET Pub Date : 2020-05-25
    Mathias Soeken; Martin Roetteler

    We generalize quantum circuits for the Toffoli gate presented by Selinger and Jones for functionally controlled NOT gates, i.e., $X$ gates controlled by arbitrary $n$-variable Boolean functions. Our constructions target the gate set consisting of Clifford gates and single qubit rotations by arbitrary angles. Our constructions use the Walsh-Hadamard spectrum of Boolean functions and build on the work

    更新日期:2020-05-25
  • Time-Sliced Quantum Circuit Partitioning for Modular Architectures
    arXiv.cs.ET Pub Date : 2020-05-25
    Jonathan M. Baker; Casey Duckering; Alexander Hoover; Frederic T. Chong

    Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and sparser connections between clusters. We exploit this clustering and the statically-known control flow of quantum programs to create tractable partitioning heuristics which map quantum circuits to modular physical

    更新日期:2020-05-25
  • scadnano: A browser-based, easily scriptable tool for designing DNA nanostructures
    arXiv.cs.ET Pub Date : 2020-05-24
    David Doty; Benjamin L Lee; Tristan Stérin

    We introduce *scadnano* (https://scadnano.org) (short for "scriptable cadnano"), a computational tool for designing synthetic DNA structures. Its design is based heavily on cadnano, the most widely-used software for designing DNA origami, with three main differences: 1. scadnano runs entirely in the browser, with *no software installation* required. 2. scadnano designs, while they can be edited manually

    更新日期:2020-05-24
  • QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation
    arXiv.cs.ET Pub Date : 2020-05-22
    Michael Booth; Jesse Berwald; Uchenna Chukwu; John Dawson; Raouf Dridi; DeYung Le; Mark Wainger; Steven P. Reinhardt

    Many organizations that vitally depend on computation for their competitive advantage are keen to exploit the expected performance of quantum computers (QCs) as soon as quantum advantage is achieved. The best approach to deliver hardware quantum advantage for high-value problems is not yet clear. This work advocates establishing quantum-ready applications and underlying tools and formulations, so that

    更新日期:2020-05-22
  • Coherent Ising machines with error correction feedback
    arXiv.cs.ET Pub Date : 2020-05-21
    Satoshi Kako; Timothée Leleu; Yoshitaka Inui; Farad Khoyratee; Yoshihisa Yamamoto

    A non-equilibrium open-dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems. However, there are two challenges in the previously proposed approach: (1) The machine can be trapped by local minima which increases exponentially with

    更新日期:2020-05-21
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