Common Hardware Research Articles

REMEDIATE: Improving Network and Middlebox Resilience With Virtualisation

ABSTRACTThe increasing demand for low‐latency, high‐bandwidth connectivity has introduced novel challenges to delivering strong resilience guarantees in production network environments. Closed hardware platforms, known as middleboxes, that lack visibility and support for state retention remain a key challenge for continuous service delivery during network failures. These middleboxes rarely employ recovery mechanisms of their own, inspiring renewed interest in the field of NFV in recent years due to this gap within the industry. The increasing availability of VNF capabilities in modern infrastructures offers an opportunity to exploit the flexibility of software and use hybrid architectures to improve resilience. REMEDIATE is a high‐availability service that propagates state between unmodified hardware middleboxes and backup PNF or VNF appliances. The platform utilises targeted packet mirroring to allow network devices to organically construct equivalent state and thus allow an easy transition between hardware and software. To demonstrate its viability, we have evaluated REMEDIATE against a wide range of common hardware middlebox use cases built using multiple open‐source packet processing frameworks. Results show upwards of 90% matching state with no observable delay to normal traffic or impact on its functionality.

Optimized Noise Suppression for Quantum Circuits

Quantum computation promises to advance a wide range of computational tasks. However, current quantum hardware suffers from noise and is too small for error correction. Thus, accurately utilizing noisy quantum computers strongly relies on noise characterization, mitigation, and suppression. Crucially, these methods must also be efficient in terms of their classical and quantum overhead. Here, we efficiently characterize and mitigate crosstalk noise, which is a severe error source in, for example, cross-resonance based superconducting quantum processors. For crosstalk characterization, we develop a simplified measurement experiment. Furthermore, we analyze the problem of optimal experiment scheduling and solve it for common hardware architectures. After characterization, we mitigate noise in quantum circuits by a noise-aware qubit routing algorithm. Our integer programming algorithm extends previous work on optimized qubit routing by swap insertion. We incorporate the measured crosstalk errors in addition to other, more easily accessible noise data in the objective function. Furthermore, we strengthen the underlying integer linear model by proving a convex hull result about an associated class of polytopes, which has applications beyond this work. We evaluate the proposed method by characterizing crosstalk noise for two chips with up to 127 qubits and leverage the resulting data to improve the approximation ratio of the Quantum Approximate Optimization Algorithm by up to 10% compared with other established noise-aware routing methods. Our work clearly demonstrates the gains of including noise data when mapping abstract quantum circuits to hardware native ones. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue on Quantum Computing. Funding: This work was supported by Bavarian state government; Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0551 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0551 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Dynamics‐Oriented Underwater Mechanoreception Interface for Simultaneous Flow and Contact Perception

The lack of a sufficient and efficient way to simultaneously perceive general underwater mechanical stimuli, physical contact, and fluidic flow has been a bottleneck for many aquatic applications. To address this challenge, dynamics‐oriented underwater mechanoreceptor interface (DOUMI), a bioinspired mechanoreception system that realizes simultaneous contact and flow perception using a single receptor, is introduced. This receptor, response‐elevated‐and‐expanded hair‐like tactile mechanoreceptor (REEM), is inspired by the mechanoreceptive mechanism of aquatic arthropods. REEM combines structural features from different mechanoreceptive sensilla, enabling it to capture a wide range of stimulus dynamics. Under different stimuli, REEM encodes stimuli dynamics as its oscillations with distinct spectral attributes. Those oscillations are efficiently transferred through mechanical processes and imaging, enabling vision‐based extraction and further analysis. Therefore, by evaluating the oscillation dynamics with tailored wavelet‐based indices, DOUMI can distinguish between contact‐ and flow‐induced oscillations at each receptor unit with 90.5% accuracy. Furthermore, DOUMI provides comprehensive 2D mechanoreception with a scalable array of REEMs, delivering capabilities like stimuli spatiotemporal visualization, flow trend detection, and scenario classification with an accuracy of 99.5%. With its robustness and operational efficiency in underwater environments, DOUMI can be easily adapted to existing applications using common materials and hardware, establishing a new, streamlined paradigm for underwater general mechanoreception.

The development and structure of the HEALthy Brain and Child Development (HBCD) Study EEG protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Electroencephalography (EEG) is one of two brain imaging modalities central to the HBCD Study. EEG records electrical signals from the scalp that reflect electrical brain activity. In addition, the EEG signal can be synchronized to the presentation of discrete stimuli (auditory or visual) to measure specific cognitive processes with excellent temporal precision (e.g., event-related potentials; ERPs). EEG is particularly helpful for the HBCD Study as it can be used with awake, alert infants, and can be acquired continuously across development. The current paper reviews the HBCD Study’s EEG/ERP protocol: (a) the selection and development of the tasks (Video Resting State, Visual Evoked Potential, Auditory Oddball, Face Processing); (b) the implementation of common cross-site acquisition parameters and hardware, site setup, training, and initial piloting; (c) the development of the preprocessing pipelines and creation of derivatives; and (d) the incorporation of equity and inclusion considerations. The paper also provides an overview of the functioning of the EEG Workgroup and the input from members across all steps of protocol development and piloting.

Experience gained about Resistive Plate Chambers ageing from the ALICE Muon TRigger/IDentifier detector

The ALICE Muon IDentifier is composed of 72 single-gap bakelite Resistive Plate Chambers, which have been operational since 2009 in avalanche mode with a Tetrafluoroethane/Isobutane/Sulphur Hexafluoride gas mixture, undergoing counting rates of the order of tens of Hz/cm2.Here, the long-term performance and stability of the RPC system will be discussed, in terms of efficiency, dark current and dark rate. An assessment of potential signs of ageing observed in the detectors will be presented, together with a summary of the most common hardware problems experienced.

Dual-Frequency, Dual-Mode Reconfigurable Digital Atmospheric Radar Receiver Design

A new dual-frequency, dual-mode reconfigurable digital receiver based on Field-Programmable Gate Array (FPGA) dynamic reconfiguration is proposed, which is based on a common hardware platform of high-bandwidth RF front-end, high-speed data acquisition, and real-time signal processing. The receiver adopts the design of dynamically reconfigurable down-conversion, filter extraction, and matched filtering in the digital domain. In this study, we completed the design and development of the digital receiver, experimental platform construction, and field detection test with hardware and software cooperation. The experimental results show that the receiver achieves full digital reception and signal processing for 53.8 MHz stratosphere–troposphere (ST) detection and 35.0 MHz meteor detection and successfully acquired the number of meteors versus time, the meteor trail, and low-altitude atmospheric radial winds. This dual-frequency, dual-mode reconfigurable digital receiver can be applied to new-generation multifunction integrated radar systems such as dual-frequency ST/meteor radars.

How to Make Rational Arguments Practical and Extractable

We investigate proof systems where security holds against rational parties instead of malicious ones. Our starting point is the notion of rational arguments, a variant of rational proofs (Azar and Micali, STOC 2012) where security holds against rational adversaries that are also computationally bounded. Rational arguments are an interesting primitive because they generally allow for very efficient protocols, and in particular sublinear verification (i.e. where the Verifier does not have to read the entire input). In this paper we aim at narrowing the gap between literature on rational schemes and real world applications. Our contribution is two-fold. We provide the first construction of rational arguments for the class of polynomial computations that is practical (i.e., it can be applied to real-world computations on reasonably common hardware) and with logarithmic communication. Techniques-wise, we obtain this result through a compiler from information-theoretic protocols and rational proofs for polynomial evaluation. The latter could be of independent interest. As a second contribution, we propose a new notion of extractability for rational arguments. Through this notion we can obtain arguments where knowledge of a witness is incentivized (rather than incentivizing mere soundness). We show how our aforementioned compiler can also be applied to obtain efficient extractable rational arguments for N P .

The Algorithms for Managing a Matrix-Based Warehouse Utilizing Standardized Transport and Storage Cells

The article explores the principles of organizing warehouse systems based on standardized transport and storage cells. It discusses the general hardware features of transport and storage cells that enable the movement of stored goods between them. The article presents a method for constructing a warehouse graph that takes into account possible directions for transferring containers with cargo between the transport and storage cells integrated into the warehouse structure, along with their common hardware characteristics. Key criteria used in determining the edge weights of the graph are described, including the basic cost of movement for each axis, cell wear and tear, cargo weight, fragility of the cargo, distance to the nearest available cells, and repairability. Algorithms are presented, the primary task of which is to determine the sequence of container movements between warehouse cells to facilitate loading and unloading operations. Simulation modeling of a warehouse with dimensions of 5×5×5 was conducted using the proposed algorithms, both with and without considering cell wear and tear parameters. The results of the simulation highlighted the significance of this criterion, allowing for extended warehouse servicing intervals and maximizing the time until the first failure.The study also investigates the possibility of optimizing the structure of such warehouse systems to meet various requirements. As part of this investigation, the structure was optimized for a warehouse with dimensions of 4×3×3.

Propose of a low cost integrating sphere

This paper proposes the development of a low cost integrating sphere used for measuring flux characteristics and luminous spectrum lamps. The proposal provides the use of alternative materials as well the replacement of measuring high level equipment for more practical and common hardware accessories, providing a reduction in the cost of development. The objective is to minimize the cost of light sources assays, currently with a high cost, for research purposes, educational issues and the opportunity to use this alternative system for validation of new products, light bulbs and/or tubular daylighting devices for lighting indoors.

Energy-Efficient Federated Training on Mobile Device

On-device deep learning technology has attracted increasing interest recently. CPUs are the most common commercial hardware on devices and many training libraries have been developed and optimized for them. However, CPUs still suffer from poor training performance (i.e., training time) due to the specific asymmetric multiprocessor. Moreover, the energy constraint imposes restrictions on battery-powered devices. With federated training, we expect the local training to be completed rapidly therefore the global model converges fast. At the same time, energy consumption should be minimized to avoid compromising the user experience. To this end, we consider energy and training time and propose a novel framework with a machine learning-based adaptive configuration allocation strategy, which chooses optimal configuration combinations for efficient ondevice training. We carry out experiments on the popular library MNN and the experimental results show that the adaptive allocation algorithm reduces substantial energy consumption, compared to all batches with fixed configurations on off-the-shelf CPUs.

Prototyping an ionization source for non-engineers.

Novel mass spectrometry (MS) based analytical platforms have enabled scientists to detect and quantify molecules within biological and environmental samples more accurately. Novel MS instrumentation starts as a prototype and, after years of development, can become a commercial product to be used by the larger MS community. Without the initial prototype, many MS-based instruments today would not be produced. Additionally, biotechnology companies are the main drivers for research, development, and production of novel instruments, but the tools for prototyping instrumentation have never been more accessible. Here, we present a tutorial on prototyping instrumentation through the case study of developing the Next Generation IR-MALDESI source to show that an engineering degree is not required to design and construct a prototype instrument with modern hardware and software. We discuss the prototyping process, the necessary skills required for efficient prototyping, and information about common hardware and software used within initial prototypes.

5D solid-state NMR spectroscopy for facilitated resonance assignment.

1H-detected solid-state NMR spectroscopy has been becoming increasingly popular for the characterization of protein structure, dynamics, and function. Recently, we showed that higher-dimensionality solid-state NMR spectroscopy can aid resonance assignments in large micro-crystalline protein targets to combat ambiguity (Klein et al., Proc. Natl. Acad. Sci. U.S.A. 2022). However, assignments represent both, a time-limiting factor and one of the major practical disadvantages within solid-state NMR studies compared to other structural-biology techniques from a very general perspective. Here, we show that 5D solid-state NMR spectroscopy is not only justified for high-molecular-weight targets but will also be a realistic and practicable method to streamline resonance assignment in small to medium-sized protein targets, which such methodology might not have been expected to be of advantage for. Using a combination of non-uniform sampling and the signal separating algorithm for spectral reconstruction on a deuterated and proton back-exchanged micro-crystalline protein at fast magic-angle spinning, direct amide-to-amide correlations in five dimensions are obtained with competitive sensitivity compatible with common hardware and measurement time commitments. The self-sufficient backbone walks enable efficient assignment with very high confidence and can be combined with higher-dimensionality sidechain-to-backbone correlations from protonated preparations into minimal sets of experiments to be acquired for simultaneous backbone and sidechain assignment. The strategies present themselves as potent alternatives for efficient assignment compared to the traditional assignment approaches in 3D, avoiding user misassignments derived from ambiguity or loss of overview and facilitating automation. This will ease future access to NMR-based characterization for the typical solid-state NMR targets at fast MAS.

Integration of Self-Adaptive Physical-Layer Key Distribution and Encryption in Optical Coherent Communication

We propose and experimentally demonstrate a compatible physical-layer secure optical communication (PLSOC) system that integrates self-adaptive physical-layer key distribution (PLKD) and encryption (PLE) in optical coherent communication. Based on bit error rate difference of QAM signals mapped by asymmetric basis state Y-00 protocol, the secret key can be secretly exchanged over public fiber links without the pre-shared keys. Moreover, we perform a parameter self-adaptive strategy for practical and dynamic PLKD. The security of the key is evaluated in the case of a fiber-tapping attack. A secure hash algorithm, SHA3-512, is used to perform privacy amplification to obtain the virtually secure key. An error-free PLKD rate reaches 39.3 Kbits/s over 300km ultra-low loss fiber. We experimentally enable the integration of the proposed PLKD scheme and quantum noise stream cipher (QNSC) with a single wavelength, same system. Q factor penalty of the integration system compared to the QNSC system is 3.7dB (optical back-to-back) and 4.8dB (300km) respectively. By exploiting a common hardware platform, with the same wavelength, the proposed PLSOC system addresses the problem that PLKD and PLE are separately performed through independent optical fiber links or wavelengths. Since only digital signal processing is used, the scheme does not require extra hardware.

Value of Service Maximization in Integrated Localization and Communication System Through Joint Resource Allocation

The rapid proliferation of smart devices and Internet of Things (IoT) applications have brought significantly increased demands for concurrent sensing, localization and communication services. To achieve multiple functions concurrently, new unified wireless systems including integrated localization and communication (ILAC) and integrated sensing and communication (ISAC) are facing the fundamental challenge of integrative resource allocation among coexisting functions and services. In addressing this challenge, an ILAC system based on the efficient allocation of the common hardware and radio resource pool for localization and communication is proposed. A novel concept, termed Value of Service (VoS), is coined to maximize the unified performance of ILAC system for diverse service provisioning including localization accuracy and communication data rate. Furthermore, the bandwidth and temporal resource allocation problem is formulated for ILAC to maximize its VoS. Specifically, the problem is treated as a mixed-integer nonlinear problem solved by an iterative joint resource allocation (JRA) strategy. In each iteration, the resource allocation is decomposed into two steps. Firstly, the bandwidth resource is optimized with a Kelly mechanism-based continuous allocation method followed by discretization. Secondly, the temporal resource is assigned with the aid of an adaptive particle swarm optimization (PSO)-based approach. Simulation results demonstrate the significant superiority of our proposed VoS evaluation metric and JRA method in ILAC system under limited resources.

A Novel Localization Technology Based on DV-Hop for Future Internet of Things

In recent years, localization has become a hot issue in many applications of the Internet of Things (IoT). The distance vector-hop (DV-Hop) algorithm is accepted for many fields due to its uncomplicated, low-budget, and common hardware, but it has the disadvantage of low positioning accuracy. To solve this issue, an improved DV-Hop algorithm—TWGDV-Hop—is put forward in this article. Firstly, the position is broadcast by using three communication radii, the hop is subdivided, and a hop difference correction coefficient is introduced to correct hops between nodes to make them more accurate. Then, the strategy of the square error fitness function is spent in calculating the average distance per hop (ADPH), and the distance weighting factor is added to jointly modify ADPH to make them more accurate. Finally, a good point set and Levy flight strategy both are introduced into gray wolf algorithm (GWO) to enhance ergodic property and capacity for unfettering the local optimum of it. Then, the improved GWO is used to evolve the place of each node to be located, further improving the location accuracy of the node to be located. The results of simulation make known that the presented positioning algorithm has improved positioning accuracy by 51.5%, 40.35%, and 66.8% compared to original DV-Hop in square, X-shaped, and O-shaped random distribution environments, respectively, with time complexity somewhat increased.

Streaming Algorithms for Subspace Analysis: Comparative Review and Implementation on IoT Devices

Subspace analysis is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal processing tasks. However, traditional subspace analysis often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing subspace analysis to be run on low-power devices such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors and edge devices.

COAC: Cross-Layer Optimization of Accelerator Configurability for Efficient CNN Processing

To achieve high accuracy, convolutional neural networks (CNNs) are increasingly growing in complexity and diversity in layer types and topologies. This makes it very challenging to efficiently deploy such networks on custom processor architectures for resource-scarce edge devices. Existing mapping exploration frameworks enable searching for the optimal execution schedules or hardware mappings of individual network layers, by optimizing each layer’s spatial (dataflow parallelization) and temporal unrolling (TU, execution order). However, these tools fail to take into account the overhead of supporting different unrolling schemes within a common hardware architecture. Using a fixed unrolling scheme across all layers is also not ideal, as this misses significant opportunities for energy and latency savings from optimizing the mapping of diverse layer types. A balanced approach assesses the right amount of mapping flexibility needed across target neural networks, while taking into account the overhead to support multiple unrollings. This article, therefore, presents cross-layer optimization of accelerator configurability (COAC), a cross-layer design space exploration and mapping framework to optimize the flexibility of neural processing architectures by balancing configurability overhead against resulting energy and latency savings for end-to-end inference. COAC does not only provide a systematical analysis of the architectural overhead in function of the supported spatial unrollings (SUs), but also builds an automated flow to find the best unrolling combination(s) for efficient end-to-end inference with limited hardware overhead. Results demonstrate that architectures with carefully optimized flexibility can achieve up to 38% energy-delay-product (EDP) savings for a set of six neural networks at the expense of a relative area increase of 9.5%.

Clustering based background learning for hyperspectral anomaly detection

In real hyperspectral images, which have a complex background with multiple classes, separation of anomalous targets from background is a challenging problem. The Clustering based Background Learning (CBL) method is proposed for hyperspectral anomaly detection in this paper to deal with this difficulty. The proposed CBL method identifies the background classes, and so facilitates detection of the anomalies. CBL is built based on this reality that anomalies comprise a limited number of pixels occupying small areas. To achieve anomalies, the spectral and spatial clustering maps are fused through a new method called Multiplicative Fusion (MF). This fusion method leverages both spectral and spatial information and makes the clustering results to support each other for anomalies detection. The synergy done in the clustering phase followed by applying an adaptive support vector machine leads to identification of various background classes. One important advantage of the CBL method is its generalization ability for anomaly detection in various sub regions of a large scene where the trained model for a sub region can be used for anomaly detection of other sub regions of the same scene. Additionally, the pre-processing and the model training steps can be performed on a common hardware without any requirement to expensive hardware for high processing power. The experimental results demonstrate that the CBL method outperforms the competing methods on various types of hyperspectral data with different spectral and spatial resolutions where a stable behaviour can be seen in detection performance of CBL on different images.

Automatic Generation of Spatial Accelerator for Tensor Algebra

Tensor algebra finds applications in various domains including machine learning applications, data analytics and others. Spatial hardware accelerators are widely used to boost the performance of tensor algebra applications. It has a complex hardware architecture and rich design space. Prior approaches based on manual implementation lead to low programming productivity, making it hard to explore the large design space. In this paper, we propose Tensorlib, a framework for generating spatial hardware accelerators for tensor algebra applications. Tensorlib is motivated by the observation that, tensor dataflows can be expressed with linear transformations, and they share common hardware modules which can be reused across different designs. Tensorlib first uses Space-Time Transformation to explore different dataflows, which can compactly represent the hardware dataflow using a transformation matrix. Next, we identify the common structures of different dataflows and build parameterized hardware module templates. Our generation framework can select the needed hardware modules for each dataflow, connect the modules using a specified interconnection pattern, and automatically generate the complete hardware accelerator design. Tensorlib remarkably improves the productivity for the development and optimization of spatial hardware architecture, providing a rich design space with trade-offs in performance, area, and power. Experiments show that Tensorlib can automatically generate hardware designs with different dataflows for a variety of tensor algebra programs. Tensorlib can achieve 318 MHz frequency and 786 GFLOP/s throughput for matrix multiplication kernel on Xilinx VU9P FPGA, which outperforms the state-of-the-art generators.

Low-Cost and High-Performance Adaptive Cruise Control Based on Inertial-Triggered Mechanism and Multi-Objective Optimization

The “accelerating-decelerating” logic adopted by the traditional adaptive cruise control system fails to exploit vehicle's inertial energy. This paper proposes a high-performance adaptive cruise control system with common hardware configuration based on inertial-triggered mechanism and multi-objective optimization. In the control strategy, a simple method is adopted to predict preceding vehicle's acceleration. On the basis of the zero-crossing points extracted from the predicted acceleration, the inertial-triggered mechanism is established to reasonably configurate host vehicle's “accelerating-inertial driving-decelerating” logic. Then, within the framework of model predictive control algorithm, multiple objectives are optimized by properly releasing the kinetic energy stored in vehicle inertia. Verification results show that when the proposed control strategy is employed, fuel economy can achieve relatively evident growth while braking time is decreased by 16.4% to efficiently improve vehicle safety.