Throughput Constraints Research Articles

A Cost-Driven Chip Partitioning Method for Heterogeneous 3D Integration

Three-dimensional integration circuit (3D IC) offers significant benefits in terms of performance and cost. Existing research in through-silicon via (TSV)-based 3D IC partitioning has focused on minimizing the number of TSVs to reduce costs. Partitioning methods based on heterogeneous integration have emerged as viable approaches for cost optimization. Leveraging mature processes to manufacture not timing-critical blocks can yield cost benefits. Nevertheless, none of the previous 3D partitioning work has focused on reducing the overall cost, including both design and manufacturing costs, for heterogeneous 3D integration. Moreover, throughput constraints have not been considered. This article presents a cost-aware integer linear programming-based formulation and a heuristic algorithm that partition the functional blocks in the design into different technological groups. Each group of functional blocks will be implemented using a particular process technology, and then integrated into a 3D IC. Our results show that 3D heterogeneous integration chip implementation can reduce overall cost while satisfying various timing constraints.

Built to last, not to scale: The long run of decentralised autonomous organisations

The FTX scandal and crypto boom-and-bust cycles have called into question the sustainability of decentralised systems like DAOs. This article argues that inherent constraints will limit DAOs’ mainstream adoption. We contend that the primacy of code in DAOs—represented by the code is law maxim—is overstated given semantic gaps in contracts. Crisis management is another serious bottleneck due to challenges of coordinating decisions without central authorities. Informal hierarchies may also emerge—in line with organisational theoretic concept of power clawing back, which would undermine decentralization objectives. Our analysis considers anonymous reputation verification, flexibility, and transparency to have more staying power and identifies scalability as the primary obstacle—stemming from high transaction costs and throughput constraints.

Cognitive routing in software defined networks using learning models with latency and throughput constraints

<p>To address latency and throughput challenges in software defined networks (SDNs), the research investigates cognitive routing's revolutionary implications. In today's data-driven world, network performance optimisation is crucial. Cognitive routing is a dynamic and potentially disruptive network management technology. Cognitive routing, strengthened by reinforcement learning and adaptive decision-making, is crucial to network efficiency and responsiveness, according to our study. The results show that cognitive routing optimises performance by limiting delay and maximising throughput. SDN application cognitive routing engine (CRE) driving forces, design, and preliminary assessment are described in this article. The CRE finds almost optimal paths for a user's quality of service (QoS) need while minimising monitoring overhead. Instead of global monitoring to find optimal paths, local monitoring achieves this. In ad-hoc networks, finding a trustworthy path reduces latency and ensures network stability. The proposed system was simulated utilising many parameters. Compared to previous SDN-based systems, end-to-end latency and ping round-trip time were better.</p>

Age of information minimization in hybrid cognitive radio networks under a timely throughput constraint

Exchanging time-critical information is prevalent in various industrial applications where low latency and timely delivery are paramount. Through this work, we consider a cognitive radio network comprised of multiple secondary users with time-sensitive traffic, and they can access the licensed channel under the hybrid interweave/underlay scheme to enhance spectrum utilization. Traffic in the secondary system is divided into two distinct categories: deadline-constrained data and status updates. Quality of service of data with expiration time, such as multimedia streams, is assessed through the timely throughput metric. However, the age of information metric is used to characterize the freshness of the status update packets, which is vital in several emerging applications. Within an interference constraint imposed by the primary user, a dynamic scheduling policy is proposed to optimize the weighted sum of the average age of information of the status update users under a strict timely throughput requirement for each user with deadline-constrained traffic. We formulate the optimization problem as a constrained Markov decision process. Then, through the drift-plus-penalty method, the problem is reduced into a series of unconstrained Markov decision problems. Finally, each subproblem is tackled using the backward dynamic programming technique. Simulation results illustrate the effect of the main system parameters, such as the PU transmitted power and transmission rate level, on the performance of the secondary system. Moreover, the model feasibility regarding the fulfillment of the constraints against PU activity is experimentally investigated under the proposed hybrid mode and classical interweave mode. The performance of the proposed policy is compared to two other low-complexity scheduling schemes, which ensure the satisfaction of the constraints; results show the performance superiority of our proposed policy.

Time Allocation Approaches for a Perceptive Mobile Network Using Integration of Sensing and Communication

One of the main challenges of popularizing the integration of sensing and communication (ISAC) network is mutual interference between the two functions. A viable solution is the time division scheme where communication and sensing are separated in time domain. This paper considers a multi-cluster ISAC network model, where the time-domain radio resources are allocated to sensing and communication. At the same time, the time resources can be reused in space-domain. Particularly, the terminals in different work phases can access radio resources of different or the same clusters simultaneously, depending on interference. In this way, the interference is isolated while the resources utilization is improved. Two different resource allocation approaches are proposed according to how interference is considered. The aim is to maximize the sensing detection probability under the constraint of network throughput. The performance improvement in terms of target detection probability brought by the proposed schemes is shown by numerical results compared with benchmark methods.

Nanoliter-Scale Sample Preparation for Single-Cell Proteomic Analysis Using Glass-Oil-Air-Droplet Chip.

Single-cell proteomic analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems' heterogeneous populations. Mass spectrometry (MS)-based proteomics is a promising alternative for quantitative single-cell proteomics. Various techniques are continually evolving to address the challenges of limited sample material, detection sensitivity, and throughput constraints. In this chapter, we describe a nanoliter-scale glass-oil-air-droplet (gOAD) chip engineered for heat tolerance, which combines droplet-based microfluidics and shotgun proteomic analysis techniques to enable multistep sample pretreatment.

Online Task Scheduling and Termination With Throughput Constraint

Online Task Scheduling and Termination With Throughput Constraint

X-ray-based overlay metrology using reciprocal space slicing analysis

Overlay serves as the pivotal performance indicator for lithography tools, and its prompt and precise measurement significantly underpins the process yield control. At present, diffraction-based overlay metrology employing optical wavelengths encounters constraints in terms of measurement sensitivity. When transitioning to x-ray wavelengths, the critical-dimension small-angle x-ray scattering (CDSAXS) method for nanostructure characterization necessitates reciprocal space mapping (RSM) and inverse problem solving, resulting in substantial throughput constraints. In this work, we propose an x-ray-based overlay metrology using reciprocal space slicing analysis (RSS), yielding high-precision overlay measurement at one single angle of incidence (AOI). Moreover, we examine the robustness of the proposed method against errors stemming from overlay target grating fabrication and measurement processes, substantiating its efficacy as a novel x-ray-based overlay metrology and unveiling the potential application of x-ray-based techniques within the realm of integrated circuit metrology.

Stiffness influence on particle separation in polydimethylsiloxane-based deterministic lateral displacement devices

Polydimethylsiloxane (PDMS) is a popular material to rapidly manufacture microfluidic deterministic lateral displacement (DLD) devices for particle separation. However, manufacturing and operation challenges are encountered with decreasing device dimensions required to separate submicron particles. The smaller dimensions, notably, cause high hydraulic resistance, resulting in significant pressure even at relatively low throughputs. This high pressure can lead to PDMS deformation, which, in turn, influences the device performance. These effects may often be overlooked in the design and operation of devices but provide a systematic source of error and inaccuracies. This study focuses in detail on these effects and investigates pillar deformation in detail. Subsequently, we discuss a potential solution to this deformation using thermal annealing to stiffen the PDMS. We evaluate the influence of stiffness on the separation performance at elevated sample flow rates with submicron particles (0.45 and 0.97 µm diameter). An excellent separation performance at high throughput is successfully maintained in stiffer PDMS-based DLD devices, while the conventional devices showed decreased separation performance. However, the increased propensity for delamination constrains the maximal applicable throughput in stiffer devices. PDMS deformation measurements and numerical simulations are combined to derive an iterative model for calculating pressure distribution and PDMS deformation. Finally, the observed separation characteristics and encountered throughput constraints are explained with the iterative model. The results in this study underline the importance of considering pressure-induced effects for PDMS-based DLD devices, provide a potential mitigation of this effect, and introduce an approach for estimating pressure-induced deformation.

Age of Synchronization Minimization Algorithms in Wireless Networks with Random Updates under Throughput Constraints.

This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) through a shared bandwidth-limited channel. Considering the random arrival of status updates, we measure the data freshness with the age of synchronization (AoS) metric; specifically, we use the time elapsed since the latest synchronization as a metric. The objective of this study is to minimize the weighted sum of the average AoS of the entire network while meeting the minimum throughput requirement of each node. We consider both the central scheduling scenario and the distributed scheduling scenario. In the central scheduling scenario, we propose the optimal stationary randomized policy when the transmission feedback is unavailable and the max-weight policy when it is available. In the distributed scenario, we propose a distributed policy. The complexity of the three scheduling policies is significantly low. Numerical simulations show that the policies can satisfy the throughput constraint in the central controlling scenario and the AoS performance of the max-weight policy is close to the lower bound.

LoFFT: Low-Voltage FFT Using Lightweight Fault Detection for Energy Efficiency

Operating at reduced voltage is an effective technique for improving the energy efficiency of computing. However, the approach is constrained by its exacerbated sensitivity to Process, Voltage, and Temperature (PVT) variations, which under throughput constraints challenges finding the energy minimizing voltage-frequency operating point. Commonly utilized design approaches for adaptive voltage scaling are based on timing slack measurement or speculation techniques that require adding extra hardware, e.g., Error Detection Sequence (EDS) circuits, that substantially increase the design complexity, and are not applicable for already fabricated designs. In this paper, instead of circuit-level techniques, a low-cost algorithmic error detection method is proposed as the enabler for reduced voltage operation of Fast Fourier Transform (FFT) accelerators.Without requiring neither gate-level nor circuit-level modifications, the method works based on an intrinsic property of the Fourier transform, i.e., Parsevalfs identity. The method is demonstrated on a System-on-Chip (SoC) that integrates a Field-Programmable Gate Array (FPGA) made to operate at reduced voltages. The fault detection capability is profiled using the demonstration test bench, implemented both as software and as hardware. In the experiments, a.43% reduction in power consumption was achieved without sacrificing the throughput and reliability. The overheads of the proposed fault detection approach scale sub-linearly with respect to FFT size and are ≤10% for 1024-point FFT.

Age of error information and throughput for truncated and layer‐coded HARQ‐based satellite‐IoT systems

SummaryThe work focuses on the simultaneous improvement on the throughput and information freshness of the promising satellite‐based Internet of Things (S‐IoT). To this end, we first propose a method based on the adaptive modulation coding (AMC) and truncated layer‐coded hybrid automatic repeat request (L‐HARQ) strategies by especially considering the difference in the channel state information (CSI) between the transmitter and receiver. On account of the large signal delay of the S‐IoT systems, this paper establishes the channel model based on the correlation of the signal‐to‐noise ratio (SNR) under the outdated channel and the ideal channel. Secondly, considering the fact that the improved backtrack decoding is used in the L‐HARQ, the classical definition of age of information (AoI) is insufficient to quantify the status update freshness, we propose the age of error information (AoEI), which is defined as the elapsed time since the generation of the lastly successfully backtrack‐decoded status update. Thirdly, we characterize the statistical descriptions of the maximum wait time of the successfully recovered status updates and the interdeparture time between the two consecutive status update packets that are lastly successfully received. Then, the closed‐form expression of average AoEI is derived under the assumption of independent and identically distributed fading channels. Finally, the Markov decision process (MDP) framework is used to optimize the packet parameters in each round of L‐HARQ process under the general fading scenario. Thereby, the optimized transmission scheme that minimizes AoI with throughput constraint is solved. The simulations investigate the effect of system parameters on the average AoEI.

A Blockchain-Based Continuous Micropayment Scheme Using Lockable Signature

A blockchain-oriented continuous micropayment system forms an integral element of the digital economy, enabling seamless transactions and encouraging minor purchases. However, due to the inherent throughput constraints of blockchain, payment channels (PCs) are customarily deployed for managing high-volume transactions in an off-chain mode. Despite this, the integration of a PC necessitates a trusted intermediary to safeguard transactional security, thereby imposing an extra security assumption as only the initial and concluding transactions are visible to other system participants. To circumvent this limitation, we introduce an innovative micropayment structure utilizing lockable signatures. Each transaction within the PC coalesces into a continuous microtransaction hash-chain (CMHC), precluding unscrupulous users from accessing transactional data during the process. Additionally, all locks within the CMHC remain concealed throughout the transaction, with all information relayed in a tamper-proof manner. We provide a comprehensive security analysis and perform a gamut of evaluations. Empirical evidence indicates that our proposed system outperforms existing state-of-the-art solutions in transaction time and verification expenses. This lockable signature-dependent micropayment system not only ensures robust security but also enhances transactional speed and efficiency, thereby catalyzing the growth of the digital economy.

Throughput performance optimization of NOMA-assisted cooperative relay system with realistic impairments

Abstract This work aims to investigate the outage and throughput performance of non-orthogonal multiple access assisted cooperative relay system (CRS-NOMA) considering the realistic impairments caused due to in-phase and quadrature-phase imbalance (IQI), channel estimation errors (CEE), and successive interference cancellation (SIC) errors. More specifically, we investigate a model in which two-phase downlink transmission is carried out in two different modes: (i) CRS-NOMA without direct links and (ii) CRS-NOMA with direct links (CRS-DLNOMA). In CRS-NOMA mode, the source broadcasts a composite NOMA signal to destination users with the assistance of a decode-and-forward (DF) relay. In contrast, in CRS-DLNOMA, direct and cooperative links are available for transmission. We derive the analytical expressions of outage probability and throughput for both the NOMA destinations to evaluate the system performance of both CRS-NOMA and CRS-DLNOMA modes of transmission. Furthermore, numerical simulations also study and validate the influence of IQI, CEE, and SIC errors on the outage and throughput performance. The simulation results verify that realistic impairments degrade the system performance, but the presence of direct link has a positive impact on outage and throughput. Additionally, we use the golden search method to optimize the power allocation factor (PAF) and transmission rate to maximize the throughput at the near user while ensuring the throughput constraint at the far user.

Scheduling Policies for AoI Minimization With Timely Throughput Constraints

In 5G and beyond communication systems, the notion of latency gets great momentum in wireless connectivity as a metric for serving real-time communications requirements. However, in many applications, research has pointed out that latency could be inefficient to handle applications with data freshness requirements. Recently, Age of Information (AoI) metric, which can capture the freshness of the data, has attracted a lot of attention. In this work, we consider mixed traffic with time-sensitive users; a deadline-constrained user, and an AoI-oriented user. To develop an efficient scheduling policy, we cast a novel optimization problem formulation for minimizing the average AoI while satisfying the timely throughput constraints. The formulated problem is cast as a Constrained Markov Decision Process (CMDP). We relax the constrained problem to an unconstrained Markov Decision Process (MDP) problem by utilizing the Lyapunov optimization theory and it can be proved that it is solved per frame by applying backward dynamic programming algorithms with optimality guarantees. In addition, we provide a low-complexity algorithm guaranteeing that the timely-throughput constraint is satisfied. Simulation results show that the timely throughput constraints are satisfied while minimizing the average AoI. Simulation results show the convergence of the algorithms for different values of the weighted factor and the trade-off between the AoI and the timely throughput.

HeterPS: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments

Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process of DNN models generally handles large-scale input data with many sparse features, which incurs high Input/Output (IO) cost, while some layers are compute-intensive. The training process generally exploits distributed computing resources to reduce training time. While heterogeneous computing resources, e.g., CPUs, GPUs of multiple types, are available for the distributed training process, the scheduling of multiple layers to diverse computing resources remains critical for the training process. To efficiently train a DNN model using the heterogeneous computing resources, we propose a distributed framework, i.e., Heterogeneous Parameter Server (HeterPS), composed of a distributed architecture and a Reinforcement Learning (RL)-based scheduling method. The advantages of HeterPS are three-fold compared with existing frameworks. First, HeterPS enables efficient training process of diverse workloads with heterogeneous computing resources. Second, HeterPS exploits an RL-based method to efficiently schedule the workload of each layer to appropriate computing resources to minimize the cost while satisfying throughput constraints. Third, HeterPS manages data storage and data communication among distributed computing resources. We carry out extensive experiments to show that HeterPS significantly outperforms state-of-the-art approaches in terms of throughput (14.5 times higher) and monetary cost (312.3% smaller).

Energy efficiency in ARQ-based multi-hop systems and the tradeoff with throughput

The minimum energy per bit (EPB) as the energy efficiency (EE) metric in an automatic retransmission request (ARQ) based multi-hop system is analyzed under power and throughput constraints. Two ARQ protocols including type-I (ARQ-I) and repetition redundancy (ARQ-RR) are considered and expressions for the optimal power allocation (PA) are obtained. Using the obtained optimal powers, the EE-throughput tradeoff (EETT) is analyzed and the EETT closed-form expressions for both ARQ protocols and in arbitrary average channel gain values are obtained. It is shown that how different throughput requirements, especially the high levels, affect the EE performance. Additionally, asymptotic analysis is made in the feasible high throughput values and lower and upper EETT bounds are derived for ARQ-I protocol. To evaluate the EE a distributed PA scenario, as a benchmark, is presented and the energy saving-gain obtained from the optimal PA in comparison with the distributed PA for ARQ-I and ARQ-RR protocols is discussed in different throughput values and node locations.

A compliance-based architecture for supporting GDPR accountability in cloud computing

The implementation of the General Data Protection Regulation (GDPR) in the cloud posed technical challenges for the design of compliance solutions. In particular, the accountability principle in the GDPR requires cloud providers to demonstrate their compliance, which implies that a GDPR compliance solution should maintain tamper-proof evidence for the massive data processing activities in cloud services. Additionally, the transparency of a compliance solution is essential for improving the trust of cloud users. Most of the existing solutions implemented their compliance logic as smart contracts on a blockchain to utilize its immutable transaction history for accountability and to gain user trust through its transparency. However, this widely adopted pattern in the solutions imposed the throughput constraint of blockchains on the implementation of compliance logic. In order to address this, we first conduct a requirement analysis of the GDPR accountability principle. After the analysis, we introduce a domain model of the principle and propose a modularized architecture to support the accountability in the cloud. Then, we present a prototype implementation of the architecture, in which a blockchain-based technique is used to provide immutability and data integrity for event records (e.g., data processing activities of cloud providers), while the compliance logic is not affected by the overhead of blockchains. Finally, we evaluate the prototype using benchmarks and analyses to investigate the throughput, resource consumption, and scalability of the architecture.

Ultra-High-Throughput Absorbance-Activated Droplet Sorting for Enzyme Screening at Kilohertz Frequencies.

Droplet microfluidics is a valuable method to "beat the odds" in high throughput screening campaigns such as directed evolution, where valuable hits are infrequent and large library sizes are required. Absorbance-based sorting expands the range of enzyme families that can be subjected to droplet screening by expanding possible assays beyond fluorescence detection. However, absorbance-activated droplet sorting (AADS) is currently ∼10-fold slower than typical fluorescence-activated droplet sorting (FADS), meaning that, in comparison, a larger portion of sequence space is inaccessible due to throughput constraints. Here we improve AADS to reach kHz sorting speeds in an order of magnitude increase over previous designs, with close-to-ideal sorting accuracy. This is achieved by a combination of (i) the use of refractive index matching oil that improves signal quality by removal of side scattering (increasing the sensitivity of absorbance measurements); (ii) a sorting algorithm capable of sorting at this increased frequency with an Arduino Due; and (iii) a chip design that transmits product detection better into sorting decisions without false positives, namely a single-layered inlet to space droplets further apart and injections of "bias oil" providing a fluidic barrier preventing droplets from entering the incorrect sorting channel. The updated ultra-high-throughput absorbance-activated droplet sorter increases the effective sensitivity of absorbance measurements through better signal quality at a speed that matches the more established fluorescence-activated sorting devices.

Joint Decoding in Downlink NOMA Systems With Finite Blocklength Transmissions for Ultrareliable Low-Latency Tasks

Future ultrareliable low-latency tasks in the Internet of Things require finite blocklength transmissions. The spectral efficiency of finite blocklength transmissions, by incorporating nonorthogonal multiple access (NOMA), can be significantly improved. In conventional NOMA systems, the successive interference cancelation (SIC) is employed for signal decoding, which is optimal for sufficient long blocklength transmissions. However, for finite blocklength transmissions, the joint decoding instead of SIC is optimal. Considering the joint decoding, we study the decoding error probability and power allocation factor optimization problem, which aims at maximizing the effective throughput at the central user under the minimum-required effective throughput constraint at the cell-edge user. We put forward a 2-D search method to find the globally optimal solution and a low-complexity alternating optimization method to find the locally optimal solution. It is illustrated that our proposed joint decoding scheme has the higher effective throughput than the conventional SIC scheme.