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  • TIGER: Topology-aware Assignment using Ising machines Application to Classical Algorithm Tasks and Quantum Circuit Gates
    arXiv.cs.ET Pub Date : 2020-09-21
    Anastasiia Butko; Ilyas Turimbetov; George Michelogiannakis; David Donofrio; Didem Unat; John Shalf

    Optimally mapping a parallel application to compute and communication resources is increasingly important as both system size and heterogeneity increase. A similar mapping problem exists in gate-based quantum computing where the objective is to map tasks to gates in a topology-aware fashion. This is an NP-complete graph isomorphism problem, and existing task assignment approaches are either heuristic

  • TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks
    arXiv.cs.ET Pub Date : 2020-09-22
    Brendan Reidy; Golareh Jalilvand; Tengfei Jiang; Ramtin Zand

    In this paper, we utilize deep convolutional neural networks (CNNs) to classify the morphology of through-silicon via (TSV) extrusion in three dimensional (3D) integrated circuits (ICs). TSV extrusion is a crucial reliability concern which can deform and crack interconnect layers in 3D ICs and cause device failures. Herein, the white light interferometry (WLI) technique is used to obtain the surface

  • Analysing and Measuring the Performance ofMemristive Integrating Amplifiers
    arXiv.cs.ET Pub Date : 2020-09-21
    Jiaqi Wang; Alexander Serb; Christos Papavassiliou; Sachin Maheshwari; Themistoklis Prodromakis

    Recording reliably extracellular neural activities isan essential prerequisite for the development of bioelectronicsand neuroprosthetic applications. Recently, a fully differential,2-stage, integrating pre-amplifier was proposed for amplifyingand then digitising neural signals. The amplifier featured a finelytuneable offset that was used as a variable threshold detector.Given that the amplifier is

  • Closed-loop spiking control on a neuromorphic processor implemented on the iCub
    arXiv.cs.ET Pub Date : 2020-09-01
    Jingyue Zhao; Nicoletta Risi; Marco Monforte; Chiara Bartolozzi; Giacomo Indiveri; Elisa Donati

    Despite neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents, the development of a fully neuromorphic artificial agent is still missing. While neuromorphic sensing and perception, as well as decision-making systems, are now mature, the control and actuation part is lagging behind. In this paper

  • Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
    arXiv.cs.ET Pub Date : 2020-09-19
    Adarsha Balaji; Shihao Song; Anup Das; Jeffrey Krichmar; Nikil Dutt; James Shackleford; Nagarajan Kandasamy; Francky Catthoor

    With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging. This is because the synaptic storage resources on a tile, viz. a crossbar, can accommodate only a fixed number of pre-synaptic connections per post-synaptic neuron. For complex SNN models that have many pre-synaptic connections per neuron,

  • On the spatiotemporal behavior in biology-mimicking computing systems
    arXiv.cs.ET Pub Date : 2020-09-18
    János Végh; Ádám J. Berki

    The payload performance of conventional computing systems, from single processors to supercomputers, reached its limits the nature enables. Both the growing demand to cope with "big data" (based on, or assisted by, artificial intelligence) and the interest in understanding the operation of our brain more completely, stimulated the efforts to build biology-mimicking computing systems from inexpensive

  • ACSS-q: Algorithmic complexity for short strings via quantum accelerated approach
    arXiv.cs.ET Pub Date : 2020-09-18
    Aritra Sarkar; Koen Bertels

    In this research we present a quantum circuit for estimating algorithmic complexity using the coding theorem method. 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. The quantum circuit design based on our earlier work that allows

  • Exploring a Double Full-Stack Communications-Enabled Architecture for Multi-Core Quantum Computers
    arXiv.cs.ET Pub Date : 2020-09-17
    Santiago RodrigoNaNoNetworking Center in Catalonia - Universitat Politècnica de Catalunya; Sergi AbadalNaNoNetworking Center in Catalonia - Universitat Politècnica de Catalunya; Eduard AlarcónNaNoNetworking Center in Catalonia - Universitat Politècnica de Catalunya; Carmen G. AlmudeverQuTech - Delft University of Technology

    Being a very promising technology, with impressive advances in the recent years, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Although continued progress in the fabrication and control of qubits is required, quantum computing scalability will depend as well on a comprehensive architectural design considering a multi-core approach

  • Reconfigurable Intelligent Surface (RIS) Assisted Wireless Coverage Extension: RIS Orientation and Location Optimization
    arXiv.cs.ET Pub Date : 2020-09-17
    Shuhao Zeng; Hongliang Zhang; Boya Di; Zhu Han; Lingyang Song

    Recently, reconfigurable intelligent surfaces (RIS) have attracted a lot of attention due to their capability of extending cell coverage by reflecting signals toward the receiver. In this letter, we analyze the coverage of a downlink RIS-assisted network with one base station (BS) and one user equipment (UE). Since the RIS orientation and the horizontal distance between the RIS and the BS have a significant

  • What Have Google's Random Quantum Circuit Simulation Experiments Demonstrated about Quantum Supremacy?
    arXiv.cs.ET Pub Date : 2020-09-15
    Jack K. Horner; John F. Symons

    Quantum computing is of high interest because it promises to perform at least some kinds of computations much faster than classical computers. Arute et al. 2019 (informally, "the Google Quantum Team") report the results of experiments that purport to demonstrate "quantum supremacy" -- the claim that the performance of some quantum computers is better than that of classical computers on some problems

  • Modulated Signals in Chemical Reaction Networks
    arXiv.cs.ET Pub Date : 2020-09-14
    Titus H. Klinge; James I. Lathrop

    Electrical engineering and molecular programming share many of the same mathematical foundations. In this paper, we show how to send multiple signals through a single pair of chemical species using modulation and demodulation techniques found in electrical engineering. Key to our construction, we provide chemical implementations of classical linear band-pass and low-pass filters with induced differential

  • Probabilistic Resistive Switching Device modeling based on Markov Jump processes
    arXiv.cs.ET Pub Date : 2020-09-14
    Vasileios Ntinas; Antonio Rubio; Georgios Ch. Sirakoulis

    In this work, a versatile mathematical framework for multi-state probabilistic modeling of Resistive Switching (RS) devices is proposed for the first time. The mathematical formulation of memristor and Markov jump processes are combined and, by using the notion of master equations for finite-states, the inherent probabilistic time-evolution of RS devices is sufficiently modeled. In particular, the

  • MERAM: Non-Volatile Cache Memory Based on Magneto-Electric FETs
    arXiv.cs.ET Pub Date : 2020-09-14
    Shaahin Angizi; Navid Khoshavi; Andrew Marshall; Peter Dowben; Deliang Fan

    Magneto-Electric FET (MEFET) is a recently developed post-CMOS FET, which offers intriguing characteristics for high speed and low-power design in both logic and memory applications. In this paper, for the first time, we propose a non-volatile 2T-1MEFET memory bit-cell with separate read and write paths. We show that with proper co-design at the device, cell and array levels, such a design is a promising

  • Circuit Design for K-coloring Problem and it's Implementation on Near-term Quantum Devices
    arXiv.cs.ET Pub Date : 2020-09-13
    Amit Saha; Debasri Saha; Amlan Chakrabarti

    Now-a-days in Quantum Computing, implementation of quantum algorithm has created a stir since Noisy Intermediate-Scale Quantum (NISQ) devices are out in the market. Researchers are mostly interested in solving NP-complete problems with the help of quantum algorithms for its speed-up. As per the work on computational complexity by Karp, if any of the NP-complete problem can be solved then any other

  • Reactive fungal wearable
    arXiv.cs.ET Pub Date : 2020-09-11
    Andrew Adamatzky; Anna Nikolaidou; Antoni Gandia; Alessandro Chiolerio; Mohammad Mahdi Dehshib

    Smart wearables sense and process information from the user's body and environment and report results of their analysis as electrical signals. Conventional electronic sensors and controllers are commonly, sometimes augmented by recent advances in soft electronics. Organic electronics and bioelectronics, especially with living substrates, offer a great opportunity to incorporate parallel sensing and

  • Modeling networks of probabilistic memristors in SPICE
    arXiv.cs.ET Pub Date : 2020-09-11
    V. J. Dowling; V. A. Slipko; Y. V. Pershin

    Efficient simulation of probabilistic memristors and their networks requires novel modeling approaches. One major departure from the conventional memristor modeling is based on a master equation for the occupation probabilities of network states [arXiv:2003.11011 (2020)]. In the present article, we show how to implement such master equations in SPICE - a general-purpose circuit simulation program.

  • Mapping the XY Hamiltonian onto a Network of Coupled Lasers
    arXiv.cs.ET Pub Date : 2020-09-10
    Mostafa Honari-Latifpour; Mohammad-Ali Miri

    In recent years there has been a growing interest in the physical implementation of classical spin models through networks of optical oscillators. However, a key missing step in this mapping is to formally prove that the dynamics of such a nonlinear dynamical system is toward minimizing a global cost function which is equivalent with the spin model Hamiltonian. Here, we introduce a minimal dynamical

  • Simphony: An open-source photonic integrated circuit simulation framework
    arXiv.cs.ET Pub Date : 2020-07-23
    Sequoia Ploeg; Hyrum Gunther; Ryan M. Camacho

    We present Simphony, a free and open-source software toolbox for abstracting and simulating photonic integrated circuits, implemented in Python. The toolbox is both fast and easily extensible; plugins can be written to provide compatibility with existing layout tools, and device libraries can be easily created without a deep knowledge of programming. We include several examples of photonic circuit

  • Forecasting timelines of quantum computing
    arXiv.cs.ET Pub Date : 2020-09-10
    Jaime Sevilla; C. Jess Riedel

    We consider how to forecast progress in the domain of quantum computing. For this purpose we collect a dataset of quantum computer systems to date, scored on their physical qubits and gate error rate, and we define an index combining both metrics, the generalized logical qubit. We study the relationship between physical qubits and gate error rate, and tentatively conclude that they are positively correlated

  • Digital Signal Processing for Molecular Communication via Chemical Reactions-based Microfluidic Circuits
    arXiv.cs.ET Pub Date : 2020-09-10
    Dadi Bi; Yansha Deng

    Chemical reactions-based microfluidic circuits are expected to provide new opportunities to perform signal processing functions over molecular domain. To realize this vision, in this article, we exploit and present the digital signal processing capabilities of chemical reactions-based microfluidic circuits. Aiming to facilitate microfluidic circuit design, we describe a microfluidic circuit using a

  • Mitiq: A software package for error mitigation on noisy quantum computers
    arXiv.cs.ET Pub Date : 2020-09-09
    Ryan LaRose; Andrea Mari; Peter J. Karalekas; Nathan Shammah; William J. Zeng

    We introduce an open-source software package for error mitigation in quantum computation using zero-noise extrapolation. Error mitigation techniques improve computational performance (with respect to noise) with minimal overhead in quantum resources by relying on a mixture of quantum sampling and classical post-processing techniques. Our error mitigation package interfaces with multiple quantum computing

  • Low-cost Active Dry-Contact Surface EMG Sensor for Bionic Arms
    arXiv.cs.ET Pub Date : 2020-09-05
    Asma M. Naim; Kithmin Wickramasinghe; Ashwin De Silva; Malsha V. Perera; Thilina Dulantha Lalitharatne; Simon L. Kappel

    Surface electromyography (sEMG) is a popular bio-signal used for controlling prostheses and finger gesture recognition mechanisms. Myoelectric prostheses are costly, and most commercially available sEMG acquisition systems are not suitable for real-time gesture recognition. In this paper, a method of acquiring sEMG signals using novel low-cost, active, dry-contact, flexible sensors has been proposed

  • SlackQ : Approaching the Qubit Mapping Problem with A Slack-aware Swap Insertion Scheme
    arXiv.cs.ET Pub Date : 2020-09-04
    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 for the design of tools to help users write quantum programs for any given quantum device. The physical constraints inherent in 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 SWAP gates

  • Virtualized Logical Qubits: A 2.5D Architecture for Error-Corrected Quantum Computing
    arXiv.cs.ET Pub Date : 2020-09-04
    Casey Duckering; Jonathan M. Baker; David I. Schuster; Frederic T. Chong

    Current, near-term quantum devices have shown great progress in recent years culminating with a demonstration of quantum supremacy. In the medium-term, however, quantum machines will need to transition to greater reliability through error correction, likely through promising techniques such as surface codes which are well suited for near-term devices with limited qubit connectivity. We discover quantum

  • Ultrafast non-volatile flash memory based on van der Waals heterostructures
    arXiv.cs.ET Pub Date : 2020-09-03
    Lan Liu; Yi Ding; Jiayi Li; Chunsen Liu; Peng Zhou

    Flash memory has become a ubiquitous solid-state memory device, it is widely used in portable digital devices, computers, and enterprise applications. The development of the information age has put forward higher requirements for memory speed and retention performance. Here, we demonstrate an ultrafast non-volatile memory based on MoS2/h-BN/multi-layer graphene (MLG) van der Waals heterostructures

  • CONTRA: Area-Constrained Technology Mapping Framework For Memristive Memory Processing Unit
    arXiv.cs.ET Pub Date : 2020-09-02
    Debjyoti Bhattacharjee; Anupam Chattopadhyay; Srijit Dutta; Ronny Ronen; Shahar Kvatinsky

    Data-intensive applications are poised to benefit directly from processing-in-memory platforms, such as memristive Memory Processing Units, which allow leveraging data locality and performing stateful logic operations. Developing design automation flows for such platforms is a challenging and highly relevant research problem. In this work, we investigate the problem of minimizing delay under arbitrary

  • Fully analog memristive circuits for optimization tasks: a comparison
    arXiv.cs.ET Pub Date : 2020-09-02
    Forrest C. Sheldon; Francesco Caravelli; Carleton Coffrin

    We introduce a Lyapunov function for the dynamics of memristive circuits, and compare the effectiveness of memristors in minimizing the function to widely used optimization software. We study in particular three classes of problems which can be directly embedded in a circuit topology, and show that memristors effectively attempt at (quickly) extremizing these functionals.

  • Turning machines
    arXiv.cs.ET Pub Date : 2020-09-02
    Irina Kostitsyna; Cai Wood; Damien Woods

    Molecular robotics is challenging, so it seems best to keep it simple. We consider an abstract molecular robotics model based on simple folding instructions that execute asynchronously. Turning Machines are a simple 1D to 2D folding model, also easily generalisable to 2D to 3D folding. A Turning Machine starts out as a line of connected monomers in the discrete plane, each with an associated turning

  • On resistive spiking of fungi
    arXiv.cs.ET Pub Date : 2020-09-01
    Andrew Adamatzky; Alessandro Chiolerio; Georgios Sirakoulis

    We study long-term electrical resistance dynamics in mycelium and fruit bodies of oyster fungi P. ostreatus. A nearly homogeneous sheet of mycelium on the surface of a growth substrate exhibits trains of resistance spikes. The average width of spikes is c.~23~min and the average amplitude is c.~1~kOhm. The distance between neighbouring spikes in a train of spikes is c.~30~min. Typically there are 4-6

  • AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators
    arXiv.cs.ET Pub Date : 2020-09-01
    Tony Liu; Amirali Amirsoleimani; Fabien Alibart; Serge Ecoffey; Dominique Drouin; Roman Genov

    An adaptive inference method for crossbar (AIDX) is presented based on an optimization scheme for adjusting the duration and amplitude of input voltage pulses. AIDX minimizes the long-term effects of memristance drift on artificial neural network accuracy. The sub-threshold behavior of memristor has been modeled and verified by comparing with fabricated device data. The proposed method has been evaluated

  • A Survey of Molecular Communication in Cell Biology: Establishing a New Hierarchy for Interdisciplinary Applications
    arXiv.cs.ET Pub Date : 2020-08-31
    Dadi Bi; Apostolos Almpanis; Adam Noel; Yansha Deng; Robert Schober

    Molecular communication (MC) engineering is inspired by the use of chemical signals as information carriers in cell biology. The biological nature of chemical signaling makes MC a promising methodology for interdisciplinary applications requiring communication between cells and other microscale devices. However, since the life sciences and communications engineering fields have distinct approaches

  • Direct CMOS Implementation of Neuromorphic Temporal Neural Networks for Sensory Processing
    arXiv.cs.ET Pub Date : 2020-08-27
    Harideep Nair; John Paul Shen; James E. Smith

    Temporal Neural Networks (TNNs) use time as a resource to represent and process information, mimicking the behavior of the mammalian neocortex. This work focuses on implementing TNNs using off-the-shelf digital CMOS technology. A microarchitecture framework is introduced with a hierarchy of building blocks including: multi-neuron columns, multi-column layers, and multi-layer TNNs. We present the direct

  • The Computational Capacity of Memristor Reservoirs
    arXiv.cs.ET Pub Date : 2020-08-31
    Forrest C. Sheldon; Artemy Kolchinsky; Francesco Caravelli

    Reservoir computing is a machine learning paradigm in which a high-dimensional dynamical system, or \emph{reservoir}, is used to approximate and perform predictions on time series data. Its simple training procedure allows for very large reservoirs that can provide powerful computational capabilities. The scale, speed and power-usage characteristics of reservoir computing could be enhanced by constructing

  • Reversible Computing with Fast, Fully Static, Fully Adiabatic CMOS
    arXiv.cs.ET Pub Date : 2020-08-28
    Michael P. Frank; Robert W. Brocato; Brian D. Tierney; Nancy A. Missert; Alexander H. Hsia

    To advance the energy efficiency of general digital computing far beyond the thermodynamic limits that apply to conventional digital circuits will require utilizing the principles of reversible computing. It has been known since the early 1990s that reversible computing based on adiabatic switching is possible in CMOS, although almost all of the "adiabatic" CMOS logic families in the literature are

  • Low-Cost Performance-Efficient Field-Programmable Pin-Constrained Digital Microfluidic Biochip
    arXiv.cs.ET Pub Date : 2020-08-31
    Alireza Abdoli; Sedigheh Farhadtoosky; Ali Jahanian

    Digital microfluidic biochips (DMFBs) are revolutionary biomedical devices towards diagnostics and point-of-care applications; the chips provide the capability of performing wide ranges of biochemistry and laboratory procedures, offering various opportunities among which to mention are automation, miniaturization and cost-affordability of bioassays. There have been various digital microfluidic biochips

  • Floating-Point Multiplication Using Neuromorphic Computing
    arXiv.cs.ET Pub Date : 2020-08-30
    Karn Dubey; Urja Kothari; Shrisha Rao

    Neuromorphic computing describes the use of VLSI systems to mimic neuro-biological architectures and is also looked at as a promising alternative to the traditional von Neumann architecture. Any new computing architecture would need a system that can perform floating-point arithmetic. In this paper, we describe a neuromorphic system that performs IEEE 754-compliant floating-point multiplication. The

  • Double-IRS Assisted Multi-User MIMO: Cooperative Passive Beamforming Design
    arXiv.cs.ET Pub Date : 2020-08-31
    Beixiong Zheng; Changsheng You; Rui Zhang

    Intelligent reflecting surface (IRS) has emerged as an enabling technology to achieve smart and reconfigurable wireless communication environment cost-effectively. Prior works on IRS mainly consider its passive beamforming design and performance optimization without the inter-IRS signal reflection, which thus do not unveil the full potential of multi-IRS assisted wireless networks. In this paper, we

  • Optical Neural Networks: The 3D connection
    arXiv.cs.ET Pub Date : 2020-08-28
    Niyazi Ulas Dinc; Demetri Psaltis; Daniel Brunner

    We motivate a new canonical strategy for integrating photonic neural networks (NNs) by leveraging 3D printing. Our believe is that a NN's parallel and dense connectivity is not scalable without 3D integration. 3D additive fabrication complemented with photonic signal transduction can dramatically augment the current capabilities of 2D CMOS and integrated photonics. Here we review some of our recent

  • Towards Building A Facial Identification System Using Quantum Machine Learning Techniques
    arXiv.cs.ET Pub Date : 2020-08-26
    Philip Easom-McCaldin; Ahmed Bouridane; Ammar Belatreche; Richard Jiang

    In the modern world, facial identification is an extremely important task in which many applications rely on high performing algorithms to detect faces efficiently. Whilst classical methods of SVM and k-NN commonly used may perform to a good standard, they are often highly complex and take substantial computing power to run effectively. With the rise of quantum computing boasting large speedups without

  • Robustness Hidden in Plain Sight: Can Analog Computing Defend Against Adversarial Attacks?
    arXiv.cs.ET Pub Date : 2020-08-27
    Deboleena Roy; Indranil Chakraborty; Timur Ibrayev; Kaushik Roy

    The ever-increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such NVM crossbars promise fast and energy-efficient in-situ matrix vector multiplications (MVM) thus alleviating the long-standing von Neuman bottleneck in today's digital hardware. However the analog nature of computing

  • Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks
    arXiv.cs.ET Pub Date : 2020-08-25
    Abhiroop Bhattacharjee; Priyadarshini Panda

    \textit{Deep Neural Networks} (DNNs) have been shown to be prone to adversarial attacks. With a growing need to enable intelligence in embedded devices in this \textit{Internet of Things} (IoT) era, secure hardware implementation of DNNs has become imperative. Memristive crossbars, being able to perform \textit{Matrix-Vector-Multiplications} (MVMs) efficiently, are used to realize DNNs on hardware

  • Electrical activity of fungi: Spikes detection and complexity analysis
    arXiv.cs.ET Pub Date : 2020-08-24
    Mohammad Mahdi Dehshibi; Andrew Adamatzky

    Oyster fungi \emph{Pleurotus djamor} generate actin potential like spikes of electrical potential. The trains of spikes might manifest propagation of growing mycelium in a substrate, transportation of nutrients and metabolites and communication processes in the mycelium network. The spiking activity of the mycelium networks is highly variable compared to neural activity and therefore can not be analysed

  • A Cost & Performance-Efficient Field-Programmable Pin-Constrained Digital Microfluidic Biochip
    arXiv.cs.ET Pub Date : 2020-08-23
    Alireza Abdoli; Ali Jahanian

    Digital microfluidic biochips (DMFBs) constitute modern generation of Lab-on-Chip (LoC) devices aimed at automation, miniaturization and cost-affordability of biochemistry and laboratory procedures. Over the course of past few years there have been various application-specific and general-purpose DMFBs aimed at reduced manufacturing costs; following the same trend this study presents a general-purpose

  • Fungal sensing skin
    arXiv.cs.ET Pub Date : 2020-08-22
    Andrew Adamatzky; Antoni Gandia; Alessandro Chiolerio

    A fungal skin is a thin flexible sheet of a living homogeneous mycelium made by a filamentous fungus. The skin could be used in future living architectures of adaptive buildings and as a sensing living skin for soft self-growing/adaptive robots. In experimental laboratory studies we demonstrate that the fungal skin is capable for recognising mechanical and optical stimulation. The skin reacts differently

  • Characterizing the Stability of NISQ Devices
    arXiv.cs.ET Pub Date : 2020-08-21
    Samudra Dasgupta; Travis S. Humble

    In this study, we focus on the question of stability of NISQ devices. The parameters that define the device stability profile are motivated by the work of DiVincenzo where the requirements for physical implementation of quantum computing are discussed. We develop the metrics and theoretical framework to quantify the DiVincenzo requirements and study the stability of those key metrics. The basis of

  • A Passive Circuit-Emulator for a Current-controlled Memristor
    arXiv.cs.ET Pub Date : 2020-07-09
    Leonardo Barboni

    A memristor is an electrical element, which has been conjectured in 1971 to complete the lumped circuit theory. Currently, researchers use memristors emulators through diodes and other passive (or active) elements to study circuits with possible attractors, chaos, and ways of implementing nonlinear transformations for low-voltage novel computing paradigms. However, to date, such passive memristor emulators

  • PROTEUS: Rule-Based Self-Adaptation in Photonic NoCs for Loss-Aware Co-Management of Laser Power and Performance
    arXiv.cs.ET Pub Date : 2020-08-17
    Sairam Sri Vatsavai; Venkata Sai Praneeth Karempudi; Ishan Thakkar

    The performance of on-chip communication in the state-of-the-art multi-core processors that use the traditional electron-ic NoCs has already become severely energy-constrained. To that end, emerging photonic NoCs (PNoC) are seen as a po-tential solution to improve the energy-efficiency (performance per watt) of on-chip communication. However, existing PNoC designs cannot realize their full potential

  • Versatile Filamentary Resistive Switching Model
    arXiv.cs.ET Pub Date : 2020-08-17
    Iosif-Angelos Fyrigos; Vasileios Ntinas; Georgios Ch. Sirakoulis; Panagiotis Dimitrakis; Ioannis G. Karafyllidis

    Memristors as emergent nano-electronic devices have been successfully fabricated and used in non-conventional and neuromorphic computing systems in the last years. Several behavioral or physical based models have been developed to explain their operation and to optimize their fabrication parameters. All existing memristor models are trade-offs between accuracy, universality and realism, but, to the

  • Breaking Barriers: Maximizing Array Utilization for Compute In-Memory Fabrics
    arXiv.cs.ET Pub Date : 2020-08-15
    Brian Crafton; Samuel Spetalnick; Gauthaman Murali; Tushar Krishna; Sung-Kyu Lim; 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

  • Quantum advantage for computations with limited space
    arXiv.cs.ET Pub Date : 2020-08-14
    Dmitri Maslov; Jin-Sung Kim; Sergey Bravyi; Theodore J. Yoder; Sarah Sheldon

    Quantum computations promise the ability to solve problems intractable in the classical setting. Restricting the types of computations considered often allows to establish a provable theoretical advantage by quantum computations, and later demonstrate it experimentally. In this paper, we consider space-restricted computations, where input is a read-only memory and only one (qu)bit can be computed on

  • Fan-out and Fan-in properties of superconducting neuromorphic circuits
    arXiv.cs.ET Pub Date : 2020-08-14
    M. L. Schneider; K. Segall

    Neuromorphic computing has the potential to further the success of software-based artificial neural networks (ANNs) by designing hardware from a different perspective. Current research in neuromorphic hardware targets dramatic improvements to ANN performance by increasing energy efficiency, speed of operation, and even seeks to extend the utility of ANNs by natively adding functionality such as spiking

  • Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
    arXiv.cs.ET Pub Date : 2020-08-11
    Lucas Eiermann; Florian Wirthmüller; Kay Massow; Gabi Breuel; Ilja Radusch

    Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation information. Current research in the field of environment mapping is concerned with two major challenges. The first one is the integration of information from different

  • Scaling of Multi-contact Phase Change Device for Toggle Logic Operations
    arXiv.cs.ET Pub Date : 2020-08-10
    Raihan Sayeed Khan; Nadim H. Kanan; Jake Scoggin; Helena Silva; Ali Gokirmak

    Scaling of two dimensional six-contact phase change devices that can perform toggle logic operations is analyzed through 2D electrothermal simulations with dynamic materials modeling, integrated with CMOS access circuitry. Toggle configurations are achieved through a combination of isolation of some contacts from others using amorphous regions and coupling between different regions via thermal crosstalk

  • Experimental Demonstration of a Reconfigurable Coupled Oscillator Platform to Solve the Max-Cut Problem
    arXiv.cs.ET Pub Date : 2020-08-10
    Mohammad Khairul Bashar; Antik Mallick; Daniel S Truesdell; Benton H. Calhoun; Siddharth Joshi; Nikhil Shukla

    In this work, we experimentally demonstrate an integrated circuit (IC) of 30 relaxation oscillators with reconfigurable capacitive coupling to solve the NP-Hard Maximum Cut (Max-Cut) problem. We show that under the influence of an external second-harmonic injection signal, the oscillator phases exhibit a bi-partition which can be used to calculate a high quality approximate Max-Cut solution. Leveraging

  • Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling
    arXiv.cs.ET Pub Date : 2020-08-04
    Qian Lou; Sarath Janga; Lei Jiang

    Nanopore genome sequencing is the key to enabling personalized medicine, global food security, and virus surveillance. The state-of-the-art base-callers adopt deep neural networks (DNNs) to translate electrical signals generated by nanopore sequencers to digital DNA symbols. A DNN-based base-caller consumes $44.5\%$ of total execution time of a nanopore sequencing pipeline. However, it is difficult

  • Accuracy and Resiliency of Analog Compute-in-Memory Inference Engines
    arXiv.cs.ET Pub Date : 2020-08-05
    Zhe Wan; Tianyi Wang; Yiming Zhou; Subramanian S. Iyer; Vwani P. Roychowdhury

    Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory (NVM) technologies have been explored for deep neural networks (DNN) to improve energy efficiency. Such architectures, however, leverage charge conservation, an operation with infinite resolution, and thus are susceptible to errors. The computations in DNN realized by analog NVM thus have high uncertainty

  • Machine Learning in Nano-Scale Biomedical Engineering
    arXiv.cs.ET Pub Date : 2020-08-05
    Alexandros-Apostolos A. Boulogeorgos; Stylianos E. Trevlakis; Sotiris A. Tegos; Vasilis K. Papanikolaou; George K. Karagiannidis

    Machine learning (ML) empowers biomedical systems with the capability to optimize their performance through modeling of the available data extremely well, without using strong assumptions about the modeled system. Especially in nano-scale biosystems, where the generated data sets are too vast and complex to mentally parse without computational assist, ML is instrumental in analyzing and extracting

  • SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks
    arXiv.cs.ET Pub Date : 2020-08-05
    Anakha V Babu; Osvaldo Simeone; Bipin Rajendran

    We discuss a high-performance and high-throughput hardware accelerator for probabilistic Spiking Neural Networks (SNNs) based on Generalized Linear Model (GLM) neurons, that uses binary STT-RAM devices as synapses and digital CMOS logic for neurons. The inference accelerator, termed "SpinAPS" for Spintronic Accelerator for Probabilistic SNNs, implements a principled direct learning rule for first-to-spike

  • Spiking neuromophic chip learns entangled quantum states
    arXiv.cs.ET Pub Date : 2020-08-03
    Stefanie Czischek; Andreas Baumbach; Sebastian Billaudelle; Benjamin Cramer; Lukas Kades; Jan M. Pawlowski; Markus K. Oberthaler; Johannes Schemmel; Mihai A. Petrovici; Thomas Gasenzer; Martin Gärttner

    Neuromorphic systems are designed to emulate certain structural and dynamical properties of biological neuronal networks, with the aim of inheriting the brain's functional performance and energy efficiency in artificial-intelligence applications [1,2]. Among the platforms existing today, the spike-based BrainScaleS system stands out by realizing fast analog dynamics which can boost computationally

  • Exploiting degeneracy to construct good ternary quantum error correcting code
    arXiv.cs.ET Pub Date : 2020-08-03
    Ritajit Majumdar; Susmita Sur-Kolay

    Quantum error-correcting code for higher dimensional systems can, in general, be directly constructed from the codes for qubit systems. What remains unknown is whether there exist efficient code design techniques for higher dimensional systems. In this paper, we propose a 7-qutrit error-correcting code for the ternary quantum system and show that this design formulation has no equivalence in qubit

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