Open Source Memory Compiler for Automatic RRAM Generation and Verification

This is the first open-source memory compilers that has been developed specifically to automate ResistiveRandom Access Memory (RRAM) generation . RRAM holds the promise of achieving high speed, high density and non-volatility . A novel RRAM architecture,additionally is proposed, and a number of generated RRAM arrays are evaluated to identify their worst case control line parasitics and worst case settlingtime across the memristors of their cells .…

On the Role of Incentives in Evolutionary Approaches to Organizational Design

This paper introduces a model of a stylized organization that is comprised of departments that autonomously allocate tasks . The organization guides the departments’ behavior by either an individualistic, a balanced, or an altruistic linear incentive scheme . Short-sighted decisions appear favorable since they do not only increase performance in the short run but also result in significantly higher performances in the long run .…

Graph theoretic algorithms for Kolmogorov operators Approximating solutions and their gradients in elliptic and parabolic problems on manifolds

We employ kernel-based approaches that use samples from a probability distribution to approximate a Kolmogorov operator on a manifold . We also employ an efficient $k$-$d$ tree algorithm to compute the sparse kernel matrix, which is a computational bottleneck . These methods only require samples from the underlying distribution and, therefore, can be applied on high dimensions or on geometrically complex manifolds when spatialdiscretizations are not available .…

SoCRATES System on Chip Resource Adaptive Scheduling using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is being increasingly applied to the problem of resource allocation for emerging System-on-Chip applications . The majority of SoC resource management approaches have been targeting makespanminimization with fixed number of jobs in the system . In contrast, SoCRATESaims at minimizing average latency in a steady-state condition while assigning tasks in the ready queue to heterogeneous resources .…

Memory Reduction using a Ring Abstraction over GPU RDMA for Distributed Quantum Monte Carlo Solver

The authors studied one such US Department of Energy mission-critical condensed matter physics application, Dynamical ClusterApproximation (DCA++) This paper discusses how device memory-bound challenges were successfully reduced by proposing an effective “all-to-all” communication method — a ring communication algorithm . The ring algorithm was optimized with sub-ring communicatorsand multi-threaded support to further reduce communication overhead and exposemore concurrency, respectively .…

Directional TGV based image restoration under Poisson noise

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction . Problem sof this type arise, for example, in microscopy or computed tomography forcarbon or glass fibres . We solve the problem by an ADMM algorithm with provenconvergence and subproblems that can be solved exactly at a low computational cost .…

Black box adversarial attacks using Evolution Strategies

In the last decade, deep neural networks have proven to be very powerful incomputer vision tasks . However, they are not robust toperturbations of the input data . Several methods able to generateadversarial samples make use of gradients, which usually are not available to an attacker in real-world scenarios .…

Traceability Technology Adoption in Supply Chain Networks

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing demand visibility, or ascertainings sustainable supplier practices . We introduce a model of the dynamics of traceability technology adoption insupply chain networks to tackle the problem of selecting the smallest set of early adopters guaranteeing broad dissemination .…

Memory Optimality for Non Blocking Containers

A bounded container maintains a collection of elements that can be inserted and extracted as long as the number of stored elements does not exceed the capacity . We consider the concurrent implementations of a bounded container more or less memory-friendly depending on how much memory they use in addition to storing the elements .…

Tracking and managing deemed abilities

A basic model can be thought of as the ongoing trace of a multi-agent system . Every state records systemic confirmations and disconfirmations of whether an acting entity is able to bring about something . Qualitative inductive reasoning is then used in order to infer what actingentities are deemed able to do .…

Verification of Distributed Quantum Programs

Distributed quantum systems and especially the Quantum Internet have the potential to fully demonstrate the power of quantumcomputing . Developing a general-purpose quantum computer is much more difficult than connecting many small quantum devices . One major challenge of implementing distributed quantum systems is programming them and verifying their correctness .…

An integration by parts formula for the bilinear form of the hypersingular boundary integral operator for the transient heat equation in three spatial dimensions

An integration by parts formula for the transient heat equation in three spatial dimensions is available in the literature, but a proof of this formula seems to be missing . The available formula contains anintegral term including the time derivative of the fundamental solution of the heat equation, whose interpretation is difficult at second glance .…

Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

This article presents the data used to evaluate the performance ofevolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms . Two experimental methods are employed to examinethe performance of ECA* against genetic algorithm for clustering++(GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation(EM) and K-means (KM) The results of the experiments performeddemonstrate some limitations in the ECA*: (i) ECA*.…

COSCO Container Orchestration using Co Simulation and Gradient Based Optimization for Fog Computing Environments

Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications . Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach schedulingdecisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios .…

On Linear Time Decidability of Differential Privacy for Programs with Unbounded Inputs

We introduce an automata model for describing interesting classes of differential privacy mechanisms/algorithms . These automata can model algorithms whose inputs can be anunbounded sequence of real-valued query answers . We consider the problem of checking whether there exists a constant $d$ such that the algorithm described by these automata are $d\epsilon$-differentially private .…

Resource Allocation and Service Provisioning in Multi Agent Cloud Robotics A Comprehensive Survey

The concept of multi-agent cloudrobotics enables robot-to-robot cooperation and creates a complementaryenvironment for the robots in executing large-scale applications . The optimal resource allocation for robotic tasks is challenging to achieve in such a complex environment . The data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-timeinteractions and impedes service performance guarantee .…

Medium Access using Distributed Reinforcement Learning for IoTs with Low Complexity Wireless Transceivers

This paper proposes a distributed Reinforcement Learning (RL) based framework that can be used for synthesizing MAC layer wireless protocols in IoT networks . The proposed framework does not rely on complex hardware capabilities such as carrier sensing and its associatedalgorithmic complexities that are often not supported in wireless transceiversof low-cost and low-energy IoT devices .…

Efficient SPARQL Autocompletion via SPARQL

At any position in the body of a SPARQL query, theautocompletion suggests matching subjects, predicates, or objects . The suggestions are context-sensitive in the sense that they lead to a non-empty result and are ranked by their relevance to the part of the query alreadytyped .…

Tuna A Static Analysis Approach to Optimizing Deep Neural Networks

We introduce Tuna, a static analysis approach to optimizing deep neuralnetwork programs . The optimization of tensor operations such as convolutionsand matrix multiplications is the key to improving the performance of deepneural networks . We use static analysis of the relative performance oftensor operations to optimize the deep learning program .…

MUSE Multi faceted Attention for Signed Network Embedding

Signed network embedding is an approach to learn low-dimensionalrepresentations of nodes in signed networks with both positive and negative links . MUSE is a MUlti-facetedattention-based Signed network Embedding framework to tackle this problem . The framework uses a joint intra- and interfacet attention mechanism to aggregate fine-grained information from neighbor nodes .…

Parallel implementation of a compatible high order meshless method for the Stokes equations

A parallel implementation of a compatible discretization scheme forsteady-state Stokes problems is presented in this work . The scheme usesgeneralized moving least squares to generate differential operators and applyboundary conditions . This meshless scheme allows a high-order convergence forboth the velocity and pressure, while also incorporates finite-difference-likesparse discretizing .…