Search

Programmable metasurfaces constitute an emerging paradigm, envisaged to become a key enabling technology for Reconfigurable Intelligent Surfaces (RIS) due to their powerful control over electromagnetic waves. The HyperSurface (HSF) paradigm takes one step further by embedding a network of customized integrated circuit (IC) controllers within the device with the aim of adding intelligence, connectivity, and autonomy. However, little is known about the traffic that the network needs to support as the target electromagnetic function or boundary conditions change. In this paper, the framework of a methodology is introduced to characterize the workload of programmable metasurfaces which is then used to analyze the beam steering HSFs. The workload characterization leads to many useful insights into traffic behavior, including the spatio-temporal load incurred and the HSF limitations in terms of fine-grained tracking of moving targets. It is observed that the traffic is inherently bursty with an uneven spatial distribution of load and that finer resolution comes at the cost of an increased but less bursty load. An indoor mobility model indicates reasonable signaling load on the deployed surfaces. Finally, a statistical analysis on the traffic patterns is performed, showing that the incoming traffic can be well represented by an ON-OFF model.

In the current contribution, we examine the feasibility of fully-energy-autonomous operation of reconfigurable intelligent surfaces (RIS) through wireless energy harvesting (EH) from incident information signals. Towards this, we first identify the main RIS energy-consuming components and present a suitable and accurate energy-consumption model that is based on the recently proposed integrated controller architecture and includes the energy consumption needed for channel estimation. Building on this model, we introduce a novel RIS architecture that enables EH through RIS unit-cell (UC) splitting. Subsequently, we introduce an EH policy, where a subset of the UCs is used for beamsteering, while the remaining UCs absorb energy. In particular, we formulate a subset allocation optimization problem that aims at maximizing the signal-to-noise ratio (SNR) at the receiver without violating the RIS’s energy consumption demands. As a problem solution, we present low-complexity heuristic algorithms. The presented numerical results reveal the feasibility of the proposed architecture and the efficiency of the presented algorithms with respect to both the optimal and very high-complexity brute-force approach and the one corresponding to random subset selection. Furthermore, the results reveal how important the placement of the RIS as close to the transmitter as possible is, for increasing the harvesting effectiveness. This work was supported by the Luxembourg National Research Fund (FNR) under the CORE project RISOTTI (ref. 14773976), the European Commission’s Horizon 2020 research and innovation programme (ARIADNE) under grant agreement No. 871464, and the Digital Futures center.

We report on electronic transport in dual-gate, artificially stacked bilayer graphene field effect transistors (BiGFETs) fabricated from large-area chemical vapor deposited (CVD) graphene. The devices show enhanced tendency to current saturation, which leads to reduced minimum output conductance values. This results in improved intrinsic voltage gain of the devices when compared to monolayer graphene FETs. We employ a physics based compact model originally developed for Bernal stacked bilayer graphene FETs (BSBGFETs) to explore the observed phenomenon. The improvement in current saturation may be attributed to increased charge carrier density in the channel and thus reduced saturation velocity due to carrier-carrier scattering.

Despite its tremendous potential, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Among other issues, there are hard limits to the number of qubits that can be integrated into a single chip. Multi-core architectures are a firm candidate for unlocking the scalability of quantum processors. Nonetheless, the vulnerability and complexity of quantum communications make this a challenging approach. A comprehensive design should imply consolidating the communications stack in the quantum computer architecture. In this paper, we explain how this vision, by entangling communications and computation in the core of the design, may help to solve the open challenges. We also summarize the first results of our application of structured design methodologies backing this vision. With our work, we hope to contribute with design guidelines that may help unleash the potential of quantum computing.

Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using very long random vectors (aka hypervectors). Among different hardware platforms capable of executing HDC algorithms, in-memory computing (IMC) systems have been recently proved to be one of the most energy-efficient options, due to hypervector manipulations in the memory itself that reduces data movement. Although implementations of HDC on single IMC cores have been made, their parallelization is still unresolved due to the communication challenges that these novel architectures impose and that traditional Networks-on-Chip and Networks-in-Package were not designed for. To cope with this difficulty, we propose the use of wireless on-chip communication technology in unique ways. We are particularly interested in physically distributing a large number of IMC cores performing similarity search across a chip, and maintaining the classification accuracy when each of which is queried with a slightly different version of a bundled hypervector. To achieve it, we introduce a novel over-the-air computing that consists of defining different binary decision regions in the receivers so as to compute the logical majority operation (i.e., bundling, or superposition) required in HDC. It introduces moderate overheads of a single antenna and receiver per IMC core. By doing so, we achieve a joint broadcast distribution and computation with a performance and efficiency unattainable with wired interconnects, which in turn enables massive parallelization of the architecture. It is demonstrated that the proposed approach allows to both bundle at least three hypervectors and scale similarity search to 64 IMC cores seamlessly, while incurring an average bit error ratio of 0.01 without any impact in the accuracy of a generic HDC-based classifier working with 512-bit vectors., This paper has been accepted at 2022 IEEE International Joint Conference on Neural Networks (IJCNN)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Traditional industry is experiencing a worldwide evolution with Industry 4.0. Wireless sensor networks (WSNs) have a main role in this evolution as an essential part of data acquisition. The way in which WSNs are powered is one of the main challenges to face if industry wants to achieve the digital transformation. Energy harvesting technologies are one of the possible solutions to this challenge. The main purpose of this paper is to present a novel method to taxonomize knowledge in the field of mechanical energy harvesting to enhance the use of energy harvesting technologies in industrial applications. The methodology is based on the analysis of key parameters and performance metrics for existing technologies. The taxonomy is applied to rail axles in order to select the energy harvesting technology that is more appropriate for this specific location, demonstrating the potential of mechanical energy harvesting technologies (MEHTs) for the railway industry, as a use case of industrial environment. In addition, the taxonomy allows to identify the upcoming challenges for research purposes while analyzing the compatibility among mechanical energy harvesting technologies in order to create hybrid harvesters.

The next generation of wireless networks is expected to tap into the terahertz (THz) band (0.1–10 THz) to satisfy the extreme latency and bandwidth density requirements of future applications. However, the development of systems in this band is challenging as THz waves confront severe spreading and penetration losses, as well as molecular absorption, which leads to strong line-of-sight requirements through highly directive antennas. Recently, reconfigurable intelligent surfaces (RISs) have been proposed to address issues derived from non-line-of-sight (non-LoS) propagation, among other impairments, by redirecting the incident wave toward the receiver and implementing virtual-line-of-sight communications. However, the benefits provided by a RIS may be lost if the network operates at multiple bands. In this article, the suitability of the RIS paradigm in indoor THz scenarios for 6G is assessed grounded on the analysis of a tunable graphene-based RIS that can operate in multiple wideband transparency windows. A possible implementation of such a RIS is provided and numerically evaluated at 0.65/0.85/1.05 THz separately, demonstrating that beam steering and other relevant functionalities are realizable with excellent performance. Finally, the challenges associated with the design and fabrication of multiwideband graphene-based RISs are discussed, paving the way to the concurrent control of multiple THz bands in the context of 6G networks.

Multi-core quantum computing has been identified as a solution to the scalability problem of quantum computing. However, interconnecting quantum chips is not trivial, as quantum communications have their share of quantum weirdness: quantum decoherence and the no-cloning theorem makes transferring qubits a harsh challenge, where every extra nanosecond counts and retransmission is simply impossible. In this paper, we present our first steps towards thorough modeling of quantum communications for multi-core quantum computers, which may be considered as a middle point between the well-known paradigms of Quantum Internet and Network-on-Chip. In particular, we stress the deep entanglement that exists between latency and error rates in quantum computing, and how this affects the quantum network design for this scenario. Moreover, we show the concomitant trade-off between computation and communication resources for a set of parameters out of state-of-the-art experimental research. The observed behavior lets us foresee the potential of multi-core quantum architectures.

Relational data present in real world graph representations demands for tools capable to study it accurately. In this regard Graph Neural Network (GNN) is a powerful tool, wherein various models for it have also been developed over the past decade. Recently, there has been a significant push towards creating accelerators that speed up the inference and training process of GNNs. These accelerators, however, do not delve into the impact of their dataflows on the overall data movement and, hence, on the communication requirements. In this paper, we formulate analytical models that capture the amount of data movement in the most recent GNN accelerator frameworks. Specifically, the proposed models capture the dataflows and hardware setup of these accelerator designs and expose their scalability characteristics for a set of hardware, GNN model and input graph parameters. Additionally, the proposed approach provides means for the comparative analysis of the vastly different GNN accelerators., Comment: ISCAS 2021

Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory characteristics that come from an interplay between dense and sparse phases of computations, the emergence of reconfigurable dataflow (aka spatial) accelerators offers promise for acceleration by mapping optimized dataflows (i.e., computation order and parallelism) for both phases. The goal of this work is to characterize and understand the design-space of dataflow choices for running GNNs on spatial accelerators in order for mappers or design-space exploration tools to optimize the dataflow based on the workload. Specifically, we propose a taxonomy to describe all possible choices for mapping the dense and sparse phases of GNN inference, spatially and temporally over a spatial accelerator, capturing both the intra-phase dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do deep-dives into the cost and benefits of several dataflows and perform case studies on implications of hardware parameters for dataflows and value of flexibility to support pipelined execution., Accepted for publication at the 36th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2022)

Recently, Graph Neural Networks (GNNs) have received a lot of interest because of their success in learning representations from graph structured data. However, GNNs exhibit different compute and memory characteristics compared to traditional Deep Neural Networks (DNNs). Graph convolutions require feature aggregations from neighboring nodes (known as the aggregation phase), which leads to highly irregular data accesses. GNNs also have a very regular compute phase that can be broken down to matrix multiplications (known as the combination phase). All recently proposed GNN accelerators utilize different dataflows and microarchitecture optimizations for these two phases. Different communication strategies between the two phases have been also used. However, as more custom GNN accelerators are proposed, the harder it is to qualitatively classify them and quantitatively contrast them. In this work, we present a taxonomy to describe several diverse dataflows for running GNN inference on accelerators. This provides a structured way to describe and compare the design-space of GNN accelerators.

In the midst of the NISQ era of quantum computers, the challenges are gravitating to encompass both architecting and full-stack engineering aspects, which are inherently algorithm-driven, so that there starts to be a convergence of bottom-up and top down design approaches, what we coin as the Quantum Architecting (QuArch) era. In face of many-fold diverse design proposals, in this paper it is postulated and proposed to apply the so-called Design Space Exploration (DSE) to the full vertical stack of quantum systems as an instrumental methodology to address such design diversity challenge. This structured design means, based upon composing a multidimensional input design space together with compressing the set of output performance metrics into an optimization-oriented overall figure of merit, provides a framework and method for optimization, for performance comparison. It yields as well a way to discriminate among alternative techniques at all layers and across layers, eventually as a structured and comprehensive design-oriented formal framework to address the quantum system design and evaluation complexity. The paper concludes by illustrating instances of this methodology in optimizing and comparing mapping techniques to address the resource-constrained current NISQ quantum chips, and to carry out a quantitative gap analysis of scalability trends aiming manycore distributed quantum architectures.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Significant effort has been devoted to the development of dynamic monitoring techniques to exploit the temporal correlation among observations with the intention of improving the energy efficiency in wireless sensor nodes. However, the unpredictable nature of these approaches can also be a drawback for their adoption in safety-critical applications. For battery-powered sensor nodes, the predictability of the battery lifetime of the node is paramount for their correct maintenance and operation. Nevertheless, little effort has been made to provide some level of predictability in the available dynamic sampling techniques. The work presented in this article aims to solve this problem by proposing a dynamic monitoring method, capable of exploiting temporal correlation, to improve its energy usage, while at the same time being able to comply with the battery lifetime constraints. The initial evaluation tests show that the proposed sampling method is able to outperform traditional non-adaptive sampling approaches in terms of energy usage, while being able to achieve its battery lifetime goals, even in the presence of disturbances in the energy consumption of the sensor node. Award-winning

The reconfigurable intelligent surface is a promising technology for the manipulation and control of wireless electromagnetic signals. In particular, it has the potential to provide significant performance improvements for wireless networks. However, to do so, a proper reconfiguration of the reflection coefficients of unit cells is required, which often leads to complex and expensive devices. To amortize the cost, one may share the system resources among multiple transmitters and receivers. In this paper, we propose an efficient reconfiguration technique providing control over multiple beams independently. Compared to time-consuming optimization techniques, the proposed strategy utilizes an analytical method to configure the surface for multi-beam radiation. This method is easy to implement, effective and efficient since it only requires phase reconfiguration. We analyze the performance for indoor and outdoor scenarios, given the broadcast mode of operation. The aforesaid scenarios encompass some of the most challenging scenarios that wireless networks encounter. We show that our proposed technique provisions sufficient improvements in the observed channel capacity when the receivers are close to the surface in the indoor office environment scenario. Further, we report a considerable increase in the system throughput given the outdoor environment. The work of Gabriele Gradoni was supported by the Royal Society under Grant INF\R2\192066. This work was supported in part by the European Commission through the H2020 RISE-6G Project under Grant 101017011 and in part by the EPSRC under Grant EP/V048937/1.

Software-Defined Metamaterials (SDMs) show a strong potential for advancing the engineered control of electromagnetic waves. As such, they are envisioned to enable a variety of exciting applications, among others in the domains of smart textiles, high-resolution structural monitoring, and sensing in challenging environments. Many of the applications envisage deformations of the SDM structures, such as their bending, stretching or rolling, which implies that the locations of metamaterial elements will be changing relative to one another. In this paper, we argue that if the metamaterial elements would be accurately localizable, this location information could potentially be utilized for enabling novel SDM applications, as well as for optimizing the control of the elements themselves. To enable their localization, we assume that these elements are controlled wirelessly through a Terahertz (THz)-operating nanonetwork. We consider the elements to be power-constrained, with their sole powering option being to harvest energy from different environmental sources. By means of simulation, we demonstrate sub-millimeter accuracy of the two-way Time of Flight (ToF)-based localization, as well as high availability of the service (i.e., consistently more than 80% of the time), which is a result of the low energy consumed in the localization process. Finally, we qualitatively characterize the latency of the proposed localization service, as well as outline several challenges and future research directions., 49 pages, 19 figures, 2 tables

In the quest of large-scale quantum computers, multi-core distributed architectures are considered a compelling alternative to be explored. A crucial aspect in such approach is the stringent demand on communication among cores when qubits need to interact, which conditions the scalability potential of these architectures. In this work, we address the question of how the cost of the communication among cores impacts on the viability of the quantum multi-core approach. Methodologically, we consider a design space in which architectural variables (number of cores, number of qubits per core), application variables for several quantum benchmarks (number of qubits, number of gates, percentage of two-qubit gates) and inter-core communication latency are swept along with the definition of a figure of merit. This approach yields both a qualitative understanding of trends in the design space and companion dimensioning guidelines for the architecture, including optimal points, as well as quantitative answers to the question of beyond which communication performance levels the multi-core architecture pays off. Our results allow to determine the thresholds for inter-core communication latency in order for multi-core architectures to outperform single-core quantum processors.

Deep neural network (DNN) models continue to grow in size and complexity, demanding higher computational power to enable real-time inference. To efficiently deliver such computational demands, hardware accelerators are being developed and deployed across scales. This naturally requires an efficient scale-out mechanism for increasing compute density as required by the application. 2.5D integration over interposer has emerged as a promising solution, but as we show in this work, the limited interposer bandwidth and multiple hops in the Network-on-Package (NoP) can diminish the benefits of the approach. To cope with this challenge, we propose WIENNA, a wireless NoP-based 2.5D DNN accelerator. In WIENNA, the wireless NoP connects an array of DNN accelerator chiplets to the global buffer chiplet, providing high-bandwidth multicasting capabilities. Here, we also identify the dataflow style that most efficienty exploits the wireless NoP's high-bandwidth multicasting capability on each layer. With modest area and power overheads, WIENNA achieves 2.2X--5.1X higher throughput and 38.2% lower energy than an interposer-based NoP design., Comment: ASPDAC '21

Nowadays, constellations and distributed networks of satellites are emerging as clear development trends in the space system market to enable augmentation, enhancement, and possibilities of new applications for future Earth Observation (EO) missions. While the adoption of these satellite architectures is gaining momentum for the attaining of ever more stringent application requirements and stakeholder needs, the efforts to analyze their benefits and suitability, and to assess their impact for future programmes remains as an open challenge to the EO community. In this context, this paper presents the mission and system architecture conceived during the Horizon 2020 ONION project, a European Union research activity that proposes a systematic approach to the optimization of EO space infrastructures. In particular, ONION addressed the design of complementary assets that progressively supplement current programs and took part in the exploration of needs and implementation of architectures for the Copernicus Space Component for EO. Among several use cases considered, the ONION project focused on proposing system architectures to provide improved revisit time, data latency and image resolution for a demanding application scenario of interest: Marine Weather Forecast (MWF). A set of promising system architectures has been subject of a comprehensive assessment, based on mission analysis expertise and detailed simulation for evaluating several key parameters such as revisit time and data latency of each measurement of interest, on-board memory evolution and power budget of each satellite of the constellation, ground station contacts and inter-satellite links. The architectures are built with several heterogeneous satellite nodes distributed in different orbital planes. Each platform can embark different instrument sets, which provide the required measurements for each use case. A detailed mission analysis has then been performed to the selected architecture for the MWF use case, including a refined data flow analysis to optimize system resources; a refined power budget analysis; a delta-V and a fuel budget analysis considering all the possible phases of the mission. This includes from the correction of launcher injection errors and acquisition of nominal satellite position inside the constellation, orbit maintenance to control altitude, collision avoidance to avoid collision with space debris objects and end-of-life (EOL) disposal to comply with EOL guidelines. The relevance of the system architecture selected for the MWF has been evaluated for three use cases of interest (Arctic sea-ice monitoring, maritime fishery pressure and aquaculture, agricultural hydric stress) to show the versatility and the feasibility of the chosen architecture to be adapted for other EO applications., This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 687490.

Metasurfaces, the ultrathin, 2D version of metamaterials, have recently attracted a surge of attention for their capability to manipulate electromagnetic waves. Recent advances in reconfigurable and programmable metasurfaces have greatly extended their scope and reach into practical applications. Such functional sheet materials can have enormous impact on imaging, communication, and sensing applications, serving as artificial skins that shape electromagnetic fields. Motivated by these opportunities, this progress report provides a review of the recent advances in tunable and reconfigurable metasurfaces, highlighting the current challenges and outlining directions for future research. To better trace the historical evolution of tunable metasurfaces, a classification into globally and locally tunable metasurfaces is first provided along with the different physical addressing mechanisms utilized. Subsequently, coding metasurfaces, a particular class of locally tunable metasurfaces in which each unit cell can acquire discrete response states, is surveyed, since it is naturally suited to programmatic control. Finally, a new research direction of software-defined metasurfaces is described, which attempts to push metasurfaces toward unprecedented levels of functionality by harnessing the opportunities offered by their software interface as well as their inter- and intranetwork connectivity and establish them in real-world applications. This work was supported by the European Union’s Horizon 2020 research and innovation programme-Future Emerging Topics (FETOPEN) under grant agreement No 736876 (VISORSURF). Financial support by the National Priorities Research Program grant No. NPRP9-383-1-083 from the Qatar National Research Fund is also acknowledged. O.T. acknowledges the financial support of the Stavros Niarchos Foundation within the framework of the project ARCHERS (“Advancing Young Researchers’ Human Capital in Cutting Edge Technologies in the Preservation of Cultural Heritage and the Tackling of Societal Challenges”).

Molecular Communications is a research area which combines the expertise of two main fields of science: biology and engineering. This multidisciplinarity, in addition to being a fruitful strength, makes knowledge exchange between researchers difficult. This issue has led to the definition of a new markup language, named Molecular Communication Markup Language (MolComML). It contributes to the harmonization of cross-disciplinary research activities. By leveraging the typical flexibility of markup languages, it specifies the essential components of generic molecular communication scenarios, by combining both numerical analysis and experimental synthesis. In this paper we illustrate the architecture of MolComML and focus on its peculiarities that allow describing molecular communications systems. In addition, we evaluate the performance of some molecular communication systems by using different simulation packages, configured by using the MolComML, in order to demonstrate both its portability and the possibility of combining different platforms for creating complex simulation tools.

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works On-chip communication remains as a key research issue at the gates of the manycore era. In response to this, novel interconnect technologies have opened the door to new Network-on-Chip (NoC) solutions towards greater scalability and architectural flexibility. Particularly, wireless on-chip communication has garnered considerable attention due to its inherent broadcast capabilities, low latency, and system-level simplicity. This work presents ORTHONOC, a wired-wireless architecture that differs from existing proposals in that both network planes are decoupled and driven by traffic steering policies enforced at the network interfaces. With these and other design decisions, ORTHONOC seeks to emphasize the ordered broadcast advantage offered by the wireless technology. The performance and cost of ORTHONOC are first explored using synthetic traffic, showing substantial improvements with respect to other wired-wireless designs with a similar number of antennas. Then, the applicability of ORTHONOC in the multiprocessor scenario is demonstrated through the evaluation of a simple architecture that implements fast synchronization via ordered broadcast transmissions. Simulations reveal significant execution time speedups and communication energy savings for 64-threaded benchmarks, proving that the value of ORTHONOC goes beyond simply improving the performance of the on-chip interconnect.

Molecular communications (MolCom) has been established as one of the novel interdisciplinary research themes with strong impact on science and technology in the last five years. For this reason, in 2016 the European Project CIRCLE (Coordinating European Research on Molecular Communications) started organizing an annual event, named Workshop on Molecular Communications , to encourage strategic development of molecular communications research in Europe with a focus on broadening the reach of the community into adjacent disciplines, such as synthetic biology, systems biology, pharmacology, inter/intra cellular communications, chemical networks, and communications engineering. The third edition of the Workshop on Molecular Communications was organized by the IDLab—Department of Information Technology of the Ghent University—IMEC on April 4–6, 2018. The workshop has provided the research community with an opportunity to meet and share their research, vision and experience in the area of molecular communications across different disciplines. In addition to invited talks and tutorials, the technical program committee accepted 19 extended abstracts for oral presentation. Among them, a total of four papers were selected for publication in this special section of IEEE Transactions on NanoBioscience after a subsequent IEEE independent peer review process. Moreover, three additional papers were selected to be included in the special issue among those submitted to it through the open call, totaling seven accepted papers for this special issue. The Guest editors sincerely thank all the participants for their appreciated contributions.

A Diffusion-based Molecular Communication (DMC) system is based on the free diffusion of particles that carry the message between the transmitter and the receivers. One of the main problems which lead to decreased data rates in such network is the Inter Symbol Interference (ISI) caused by the heavy tail of the impulse response. In this paper, we study the influence of neighboring absorbing receivers upon the Inter Symbol Interference (ISI) of a Diffusion-based Molecular Communication (DMC) point-to-point link. It is shown that neighboring absorbing receivers have a noticeable impact on reducing the tail of the detected pulse-shape and, hence, higher achievable throughput are reachable.

The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use cases that illustrate its applicability and benefits. We also present simple experimental results that support its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN)., 8 pages, 22 references, 6 figures and 1 table

This paper presents a steady-state analysis of the isolated class-E $^2$ converter outside nominal operation. By transforming the isolated class-E $^2$ converter into the typical topology of the class-E inverter, the switching patterns can be comprehended on the parameter spaces easily. The output power and the power-conversion efficiency can be expressed as functions of circuit parameters such as coupling coefficient of the transformer, load resistance, and operating frequency. The class-E $^2$ wireless power transfer (WPT) system was designed and implemented in this paper for verifying the analytical expressions. The analytical predictions of system performance against coupling-coefficient and load-resistance variations agreed with PSpice simulation and experimental results quantitatively, which showed the validity of the analytical expressions. Comparisons between the class-E $^2$ and class-D-E WPT systems are shown along with their discussions. Additionally, two application examples of the analytical expressions are introduced in this paper, which shows the usefulness of the analytical equations.

Drug delivery is one of the most important applications of molecular communication. Drug transmitters have limited resources in terms of energy and reservoir and these limitations should be taken into consideration when designing a drug delivery system. Drug molecules may also be expensive and releasing a large amount of them can have harmful effects on the healthy parts of the body. In this paper, we consider a multiple transmitter local drug delivery system in which the nearest transmitters to a randomly located tumor are activated to release drug molecules and guarantee the Least Effective Concentration (LEC) in every part of the tumor. We propose two different scenarios: a single transmitter drug delivery system for which the optimal rate of the transmitting nanomachine and the optimal density of deployed nanomachines are derived through formulations and simulations. Poisson distributed as well as regular square and hexagon grid deployments are investigated. We then extend it to a multiple transmitter drug delivery system for which the optimal allocated rate to each releasing transmitter is derived in order to minimize the total rate of release and maintain LEC in every part of the tumor. It is shown that activating multiple transmitters leads to a reduction in the total optimal release rate of drug molecules as well as improving the time duration between consecutive administrations.

Deep Neural Networks have flourished at an unprecedented pace in recent years. They have achieved outstanding accuracy in fields such as computer vision, natural language processing, medicine or economics. Specifically, Convolutional Neural Networks (CNN) are particularly suited to object recognition or identification tasks. This, however, comes at a high computational cost, prompting the use of specialized GPU architectures or even ASICs to achieve high speeds and energy efficiency. ASIC accelerators streamline the execution of certain dataflows amenable to CNN computation that imply the constant movement of large amounts of data, thereby turning on-chip communication into a critical function within the accelerator. This paper studies the communication flows within CNN inference accelerators of edge devices, with the aim to justify current and future decisions in the design of the on-chip networks that interconnect their processing elements. Leveraging this analysis, we then qualitatively discuss the potential impact of introducing the novel paradigm of wireless on-chip network in this context., Comment: ICECS2019

Recent emergence of metasurfaces has enabled the development of ultra-thin flat optical components through different wavefront shaping techniques at various wavelengths. However, due to the non-adaptive nature of conventional metasurfaces, the focal point of the resulting optics needs to be fixed at the design stage, thus severely limiting its reconfigurability and applicability. In this paper, we aim to overcome such constraint by presenting a flat reflective component that can be reprogrammed to focus terahertz waves at a desired point in the near-field region. To this end, we first propose a graphene-based unit cell with phase reconfigurability, and then employ the coding metasurface approach to draw the phase profile required to set the focus on the target point. Our results show that the proposed component can operate close to the diffraction limit with high focusing range and low focusing error. We also demonstrate that, through appropriate automation, the reprogrammability of the metamirror could be leveraged to develop compact terahertz scanning and imaging systems, as well as novel reconfigurable components for terahertz wireless communications., Comment: 10 pages, 5 figures, published in Scientific Reports (Nature)

Metasurfaces, the two-dimensional counterpart of metamaterials, have caught great attention thanks to their powerful capabilities on manipulation of electromagnetic waves. Recent times have seen the emergence of a variety of metasurfaces exhibiting not only countless functionalities, but also a reconfigurable response. Additionally, digital or coding metasurfaces have revolutionized the field by describing the device as a matrix of discrete building block states, thus drawing clear parallelisms with information theory and opening new ways to model, compose, and (re)program advanced metasurfaces. This paper joins the reconfigurable and digital approaches, and presents a metasurface that leverages the tunability of graphene to perform beam steering at terahertz frequencies. A comprehensive design methodology is presented encompassing technological, unit cell design, digital metamaterial synthesis, and programmability aspects. By setting up and dynamically adjusting a phase gradient along the metasurface plane, the resulting device achieves beam steering at all practical directions. The proposed design is studied through analytical models and validated numerically, showing beam widths and steering errors well below 10 degrees and 5% in most cases. Finally, design guidelines are extracted through a scalability analysis involving the metasurface size and number of unit cell states., Comment: 12 pages

Recent developments in nanotechnology herald nanometer-sized devices expected to bring light to a number of groundbreaking applications. Communication with and among nanodevices will be needed for unlocking the full potential of such applications. As the traditional communication approaches cannot be directly applied in nanocommunication, several alternative paradigms have emerged. Among them, electromagnetic nanocommunication in the terahertz (THz) frequency band is particularly promising, mainly due to the breakthrough of novel materials such as graphene. For this reason, numerous research efforts are nowadays targeting THz band nanocommunication and consequently nanonetworking. As it is expected that these trends will continue in the future, we see it beneficial to summarize the current status in these research domains. In this survey, we therefore aim to provide an overview of the current THz nanocommunication and nanonetworking research. Specifically, we discuss the applications envisioned to be supported by nanonetworks operating in the THz band, together with the requirements such applications pose on the underlying nanonetworks. Subsequently, we provide an overview of the current contributions on the different layers of the protocol stack, as well as the available channel models and experimentation tools. As the final contribution, we identify a number of open research challenges and outline several potential future research directions., Comment: 37 pages, 11 figures, 7 tables

As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment. This research was funded by the European Commission grant number H2020-FETOPEN736876 (VISORSURF) and by ICREA under the ICREA Academia program.

Wireless RF power transfer requires the deployment of multiple energy transmitters (ETs) to cover an entire area of interest. This letter aims at bounding the minimum cumulative power that ETs need to inject into the network, such that the recipient nodes harvest sufficient power to operate. The main findings are that, in the worst case, this scales as $O(s^{1-\alpha /2})$ , where $s$ and $\alpha $ are the number of ETs and the channel path loss. That is, the overall power decreases with the number of ETs. It is also shown that sophisticated design for power transmission can further improve the scalability by $s^{-1}$ .

Resonant Inductive Coupling Wireless Power Transfer (RIC-WPT)is a key technology to provide an efficient wireless power channel to consumer electronics, biomedical implants and wireless sensor networks. Due to its non radiative nature, RIC Wireless Power Transfer has been considered safe for humans when adhered to magnetic health radiation safety regulations (Christ et al., 2013), unveiling a large range of potential applications in which this technology could be used. However, current deployments are limited to point-to-point links and do not explore the capabilities of Multi-Node RIC-WPT Systems. In such a system, the multi-path relaying effect between different nodes could effectively improve the performance of the link in terms of power transferred to the load and power transfer efficiency. However, depending on the impedance and resonant frequency of the nodes that generate the multi-path effect, these nodes could also act as interfering objects, therefore (a) making the transmitter and/or receiver act as a pass-band filter and (b) losing part of the transmitter magnetic field through coupling to the interfering node. In this paper, a circuit-based analytical model that predicts the behavior of a Multi-Node Resonant Inductive Coupling link is proposed and used to perform a design-space exploration of the multi-path relaying effect in RIC Wireless Power Transfer Systems.

In shared-memory multiprocessing, fine-grain synchronization is challenging because it requires frequent communication. As technology scaling delivers larger manycore chips, such pattern is expected to remain costly to support. In this paper, we propose to address this challenge by using on-chip wireless communication. Each core has a transceiver and an antenna to communicate with all the other cores. This environment supports very low latency global communication. Our architecture, called WiSync, uses a per-core Broadcast Memory (BM). When a core writes to its BM, all the other 100+ BMs get updated in less than 10 processor cycles. We also use a second wireless channel with cheaper transfers to execute barriers efficiently. WiSync supports multiprogramming, virtual memory, and context switching. Our evaluation with simulations of 128-threaded kernels and 64-threaded applications shows that WiSync speeds-up synchronization substantially. Compared to using advanced conventional synchronization, WiSync attains an average speedup of nearly one order of magnitude for the kernels, and 1.12 for PARSEC and SPLASH-2.