Satisfy Timing Constraints Research Articles

An Investigation of Clock Skew Using a Wirelength-Aware Floorplanning Process in the Pre-Placement Stages of MSV Layouts

Managing the timing constraints has become an important factor in the physical design of multiple supply voltage (MSV) integrated circuits (IC). Clock distribution and module scheduling are some of the conventional methods used to satisfy the timing constraints of a chip. In this paper, we propose a simulated annealing-based MSV floorplanning methodology for the design of ICs within the timing budget. Additionally, we propose a modified SKB tree representation for floorplanning the modules in the design. Our algorithm finds the optimal dimensions and position of the clocked modules in the design to reduce the wirelength and satisfy the timing constraints. The proposed algorithm is implemented in IWLS 2005 benchmark circuits and considers power, wirelength, and timing as the optimization parameters. Simulation results were obtained from the Cadence Innovus digital system taped-out at 45 nm. Our simulation results show that the proposed algorithm satisfies timing constraints through a 30.6% reduction in wirelength.

A measurement study of offloading virtual network functions to the edge

The deployment of virtual network functions (VNFs) at edge servers potentially impairs the performance of latency-sensitive applications due to their computational cost. This work considers a new approach to addressing this problem that provides line rate acceleration of VNFs by employing field-programmable gate array (FPGA) equipped edge servers. This approach has been validated by practical use cases with both TCP and UDP as underlying protocol on a physical testbed environment. We examine the performance implications of executing a security VNF at an FPGA-equipped edge server. We experimentally demonstrate reduced VNF execution latency and energy consumption for a real-time video streaming application in comparison with a software-only baseline. In particular, the results show that the approach lowers VNF execution latency and power consumption at the edge by up to 44% and 76%, respectively, in our experiments while satisfying time constraints and maintaining confidentiality with high scalability. We also highlight the potential research challenges to make this approach viable in practice.

Generating new classes of fixed-time stable systems with predefined upper bound for the settling time

This paper aims to provide a methodology for generating autonomous and non-autonomous systems with a fixed-time stable equilibrium point where an Upper Bound of the Settling Time (UBST) is set a priori as a parameter of the system. Furthermore, some conditions for such an upper bound to be the least one are provided. This construction procedure is a relevant contribution compared with traditional methodologies for generating fixed-time algorithms satisfying time constraints since current estimates of an UBST may be too conservative. The proposed methodology is based on time-scale transformations and Lyapunov analysis. It allows the presentation of a broad class of fixed-time stable systems with predefined UBST, placing them under a common framework with existing methods using time-varying gains. To illustrate the effectiveness of our approach, we generate novel, autonomous and non-autonomous, fixed-time stable algorithms with predefined least UBST.

Stochastic Buffering for Bundled SWCNT Interconnects Considering Unidimensional Fabrication Variation

The heterogeneous system architecture which leverages multicore computing paradigm has become increasingly popular. Nevertheless, timing minimization is still a critical design challenge. Buffer insertion for bundled single-walled carbon nanotubes (SWCNTs) is capable of significantly improving circuit timing of signal nets with limited buffer deployment. However, due to the imperfection of fabricating long straight carbon nanotubes, there exist significant variations on the critical CNT geometric parameters such as the diameter and density, which will affect the circuit performance. On the other hand, the prevailing CNT fabrication uses Chemical Vapor Deposition, where the unidimensional spatial correlation manifests strongly. In this work, a unidimensional variation aware stochastic SWCNT interconnects buffering algorithm is developed to handle fabrication variations of CNTs in buffer insertion. To improve its time complexity, a novel importance sampling based timing evaluation technique is proposed considering unidimensional correlations of variations. The simulation results demonstrate that the unidimensional variation aware importance sampling based stochastic SWCNT interconnects buffering algorithm on average saves more than 30 percent buffer area over copper buffering while satisfying timing constraints. In addition, our proposed stochastic algorithm achieves much better performance than the best case design and the worst case design in terms of timing and buffer cost.

JAMS-SG

Time-triggered automotive networks use time-triggered protocols (FlexRay, TTEthernet, etc.) for periodic message transmissions that often originate from safety and time-critical applications. One of the major challenges with time-triggered transmissions is jitter, which is the unpredictable delay-induced deviation from the actual periodicity of a message. Failure to account for jitter can be catastrophic in time-sensitive systems, such as automotive platforms. In this article, we propose a novel scheduling framework (JAMS-SG) that satisfies timing constraints during message delivery for both jitter-affected time-triggered messages and high-priority event-triggered messages in automotive networks. At design time, JAMS-SG performs jitter-aware frame packing (packing of multiple signals from Electronic Control Units (ECUs) into messages) and schedules synthesis with a hybrid heuristic. At runtime, a Multi-Level Feedback Queue (MLFQ) handles jitter-affected time-triggered messages and high-priority event-triggered messages that are scheduled using a runtime scheduler. Our simulation results, based on messages and network traffic data from a real vehicle, indicate that JAMS-SG is highly scalable and outperforms the best-known prior work in the area in the presence of jitter.

Heterogeneous FPGA-Based Cost-Optimal Design for Timing-Constrained CNNs

Field programmable gate array (FPGA) has been one of the most popular platforms to implement convolutional neural networks (CNNs) due to its high performance and cost efficiency; however, limited by the on-chip resources, the existing single-FPGA architectures cannot fully exploit the parallelism in CNNs. In this paper, we explore heterogeneous FPGA-based designs to effectively leverage both task and data parallelism, such that the resultant system can achieve the minimum cost while satisfying timing constraints. In order to maximize the task parallelism, we investigate two critical problems: 1) buffer placement , where to place buffers to partition CNNs into pipeline stages and 2) task assignment , what type of FPGA to implement different CNN layers. We first formulate the system-level optimization problem with a mixed integer linear programming model. Then, we propose an efficient dynamic programming algorithm to obtain the optimal solutions. On top of that, we devise an efficient algorithm that exploits data parallelism within CNN layers to further improve cost efficiency. Evaluations on well-known CNNs demonstrate that the proposed techniques can obtain an average of 30.82% reduction in system cost under the same timing constraint, and an average of 1.5 times speedup in performance under the same cost budget, compared with the state-of-the-art techniques.

Test case prioritization and selection technique in continuous integration development environments: a case study

Regression testing is a very important activity in continuous integration development environments. Software engineers frequently integrate new or changed code that involves in a new regression testing. Furthermore, regression testing in continuous integration development environments is together with tight time constraints. It is also impossible to re-run all the test cases in regression testing. Test case prioritization and selection technique are often used to render continuous integration processes more cost-effective. According to multi objective optimization, we present a test case prioritization and selection technique, TCPSCI, to satisfy time constraints and achieve testing goals in continuous integration development environments. Based on historical failure data, testing coverage code size and testing execution time, we order and select test cases. The test cases of the maximize code coverage, the shorter execution time and revealing the latest faults have the higher priority in the same change request. The case study results show that using TCPSCI has a higher cost-effectiveness comparing to the manually prioritization.

Predictable Shared Cache Management for Multi-Core Real-Time Virtualization

Real-time virtualization has gained much attention for the consolidation of multiple real-time systems onto a single hardware platform while ensuring timing predictability. However, a shared last-level cache (LLC) on modern multi-core platforms can easily hamper the timing predictability of real-time virtualization due to the resulting temporal interference among consolidated workloads. Since such interference caused by the LLC is highly variable and may have not even existed in legacy systems to be consolidated, it poses a significant challenge for real-time virtualization. In this article, we propose a predictable shared cache management framework for multi-core real-time virtualization. Our framework introduces two hypervisor-level techniques, vLLC and vColoring, that enable the cache allocation of individual tasks running in a virtual machine (VM), which is not achievable by the current state of the art. Our framework also provides a cache management scheme that determines cache allocation to tasks, designs VMs in a cache-aware manner, and minimizes the aggregated utilization of VMs to be consolidated. As a proof of concept, we implemented vLLC and vColoring in the KVM hypervisor running on x86 and ARM multi-core platforms. Experimental results with three different guest OSs (i.e., Linux/RK, vanilla Linux, and MS Windows Embedded) show that our techniques can effectively control the cache allocation of tasks in VMs. Our cache management scheme yields a significant utilization benefit compared to other approaches while satisfying timing constraints.

Reducing the Complexity of Dataflow Graphs Using Slack-Based Merging

There exist many dataflow applications with timing constraints that require real-time guarantees on safe execution without violating their deadlines. Extraction of timing parameters (offsets, deadlines, periods) from these applications enables the use of real-time scheduling and analysis techniques, and provides guarantees on satisfying timing constraints. However, existing extraction techniques require the transformation of the dataflow application from highly expressive dataflow computational models, for example, Synchronous Dataflow (SDF) and Cyclo-Static Dataflow (CSDF) to Homogeneous Synchronous Dataflow (HSDF). This transformation can lead to an exponential increase in the size of the application graph that significantly increases the runtime of the analysis. In this article, we address this problem by proposing an offline heuristic algorithm called slack-based merging . The algorithm is a novel graph reduction technique that helps in speeding up the process of timing parameter extraction and finding a feasible real-time schedule, thereby reducing the overall design time of the real-time system. It uses two main concepts: (a) the difference between the worst-case execution time of the SDF graph’s firings and its timing constraints (slack) to merge firings together and generate a reduced-size HSDF graph, and (b) the novel concept of merging called safe merge , which is a merge operation that we formally prove cannot cause a live HSDF graph to deadlock. The results show that the reduced graph (1) respects the throughput and latency constraints of the original application graph and (2) typically speeds up the process of extracting timing parameters and finding a feasible real-time schedule for real-time dataflow applications. They also show that when the throughput constraint is relaxed with respect to the maximal throughput of the graph, the merging algorithm is able to achieve a larger reduction in graph size, which in turn results in a larger speedup of the real-time scheduling algorithms.

An Efficient and Effective Methodology to Control Turn-On Sequence of Power Switches for Power Gating Designs

As technology advances, power consumption becomes a big challenge in modern very large-scale integration designs. To resolve this problem, power-gated technology has been widely adopted in circuit designs. Since the turn-on sequence of power switches has a great impact on the rush current, wake-up, and sequence times of a power gating design, this paper proposes a methodology to construct a hybrid routing structure to connect power switches. Our hybrid routing structure can induce less rush current and satisfy timing constraints because a better daisy chain is constructed. To find members of the daisy chain, an integer linear programming algorithm is used to pick up suitable power switches. To determine suitable depth of a daisy chain, we propose a model for a power gating design and induct precise equations to estimate voltage and rush current equations according to the model. All of our experiments are based on industrial designs and measured by vendor tools. The experimental results demonstrate the efficiency and effectiveness of our design methodology.

Time and Energy Management During Approach: A Human-in-the-Loop Study

Time and energy managed operations is a new integrated planning and guidance concept that optimizes the vertical aircraft trajectory to achieve a continuous engine-idle descent while satisfying time constraints. To investigate acceptance of the concept and the influence of pilot involvement on performance, time and energy managed operations were evaluated in a real-time simulation involving nine airline pilots. Three human–machine interface variants were tested; these varied in the level of information presented. The results were compared to computer simulations with a zero-delay pilot response model, to evaluate the effect of pilot reaction time variation on performance and environmental impact. Results indicated that the effects of pilot response delays are limited. Pilots preferred the human–machine interface variant that included a timer to support accurate selection of flaps and gear. Although the timer did not significantly affect system performance, it did reduce variation in the pilot response. Results further indicated that modifications to the time and energy managed operations concept are possible to further improve time performance.

Energy Efficiency Optimization for Communication of Air-Based Information Network with Guaranteed Timing Constraints

In air-based information network of the Space-Air-Ground Integrated Network, it is critical to select an efficient communication link for each communication. However, some communication may not have fixed rules. In this article, we build a model of the air-based information network, and use the dynamic programming to select the most efficient communication link by minimizing the energy consumption in the communication model. In the communication model, energy consumption is associated with the distance between aircrafts and the relative velocity of the aircrafts. There is a given example used to describe the communication subnetworks of aircrafts in the air-based information network, and how each subnetwork containing multiple aircrafts communicates efficiently with the optimization by using the presented algorithms. The experiments show that the presented algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, there has been an achievement that an average reduction of 9.9 % per 20 time units in the 100-subnetwork model satisfying timing constraints based on the presented algorithm.

New Deadline-Aware Energy-Consumption Optimization Model and Genetic Algorithm Under Cloud Computing

With large-scale data centers widely deployed around the world, their huge energy consumption becomes a primary concern. Effective resource allocation and scheduling is one of the key to solve this problem. However, existing studies on this topic are relatively rare. In this paper, a new deadline-aware energy-consumption optimization model is designed, which optimizes both the idle and execution energy consumption of servers. To save the idle energy consumption, we propose a new virtual machine deployment algorithm for mapping virtual machines to a constrained packing problem with multidimensional variables. In the proposed genetic algorithm, in order to improve the diversity of the population, we select some of the individuals which do not satisfy time constraints but have low energy consumption into the next generation. To save the execution energy consumption, we adopt the technique of dynamic voltage and frequency scaling. Finally, experimental results show that compared with the existing algorithms, the proposed one greatly reduces the total energy consumption of data centers under the time constraints of tasks.

Real-Time Bid Optimization for Group-Buying Ads

Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by daily-deal providers. Unlike traditional web ads, the advertiser’s profits for group-buying ads depend on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts and dynamically vary the bids to maximize profits. Furthermore, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real-time ad selection, admissibility, and real-time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection, and admission strategies on a multimillion click stream of 935 ads. The proposed real-time bidding, selection, and admissibility show significant profit increases over the existing strategies. Further experiments illustrate the robustness of the bidding and acceptable computation timings.

Energy Efficient Task Assignment with Guaranteed Probability Satisfying Timing Constraints for Embedded Systems

The trade-off between system performance and energy efficiency (service time) is critical for battery-based embedded systems. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments (e.g., fluctuant network bandwidth and different user inputs). By adopting a probabilistic approach, this paper proposes a model and a set of algorithms to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with a guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task. Finally, to escape from local optima, a local search with restarts searches the optimal solution from candidate solutions by updating the objective function, until the stop criteria are reached or a time bound is elapsed. The experimental results demonstrate that for probability 1.0, our approach yields slightly better results than the well-known algorithms like ASAP/ALAP (As Soon As Possible/As Late As Possible) and ILP (Integer Linear Programming) with/without DVS (Dynamic Voltage Scaling). However, for probabilities 0.8 and 0.9, our approach significantly outperforms those algorithms (maximum improvement of 50.3 percent).

Effective Project Scheduling Under Workspace Congestion and Workflow Disturbance Factors

Effective project management implies the use of advanced planning and scheduling methods that allow to determine feasible sequences of activities and to complete a project on time and on budget. Traditional scheduling tools like fundamental Critical Path Method (CPM) and various methods for Resource Constrained Project Scheduling Problem (RCPSP) and Time Constrained Project Scheduling Problem (TCPSP) have many shortcomings for construction projects where spatial factor plays a critically important role. Previous attempts to interpret space as a specific resource were successful for particular problems of line-of-balance scheduling, space scheduling, dynamic layout planning, horizontal and vertical logic scheduling, workspace congestion mitigating, scheduling multiple projects with movable resources, spatial scheduling of repeated and grouped activities and motion planning. However, none of these methods considers the spatio-temporal requirements in a holistic framework of generic RCPSP problem and provides feasible results accounting for workspace and workflow factors. In this paper we start with the classical RCPSP statement and then present mathematically strong formalisation of the extended generalised problem, taking into account workspace congestion and workflow disturbance constraints specified in practically meaningful and computationally constructive ways. For the generalised RCPSP problem an effective scheduling method is proposed. The method tends to minimise the project makespan while satisfying timing constraints and precedence relations, not exceeding resource utilisation limits, avoiding workspace congestions and keeping workflows continuous. The method reuses so-called serial scheduling scheme and provides for additional computational routines and heuristic priority rules to generate feasible schedules satisfying all the imposed requirements. Advantages of the method and prospects for its application to industrial needs are outlined in the paper too.

Комплексный метод составления расписаний для сложных индустриальных программ с учетом пространственно-временных ограничений

Effective project management implies the use of advanced planning and scheduling methods that allow to determine the feasible sequences of activities and to complete a project on time and on budget. Traditional scheduling tools like fundamental Critical Path Method (CPM) and various methods for Resource Constrained Project Scheduling Problem (RCPSP) and Time Constrained Project Scheduling Problem (TCPSP) have many shortcomings for the construction projects where spatial factor plays critically important role. Previously taken attempts to interpret space as a specific resource were successful for particular problems of line-of-balance scheduling, space scheduling, dynamic layout planning, horizontal and vertical logic scheduling, workspace congestion mitigating, scheduling multiple projects with movable resources, spatial scheduling of repeated and grouped activities, motion planning. However, none of these methods considers the spatio-temporal requirements in a holistic framework of generic RCPSP problem and provides feasible results accounting for workspace and workflow factors. In the paper we start with the classical RCPSP statement and then present mathematically strong formalization of the extended generalized problem taking into account workspace congestion and workflow disturbance constraints specified in practically meaningful and computationally constructive ways. For the generalized RCPSP problem an effective scheduling method is proposed. The method tends to minimize the project makespan while satisfying timing constraints and precedence relations, not exceeding resource utilization limits, avoiding workspace congestion and keeping workflows continuous. The method reuses so-called serial scheduling scheme and provides for additional computational routines and heuristic priority rules to generate feasible schedules satisfying all the imposed requirements. In order to evaluate the method, benchmark sets are proposed and investigated. Advantages of the method and prospects for its application to industrial needs are outlined in the paper too.

Vehicle fleet sizing for automated material handling systems to minimize cost subject to time constraints

Vehicle fleet sizing for an Automated Material Handling System (AMHS) is an important but challenging problem due to the complexity of AMHS design and uncertainty involved in the production process; e.g., random processing time. For a complex manufacturing system such as semiconductor manufacturing, the problem is even more complex. This article studies the vehicle fleet sizing problem in semiconductor manufacturing and proposes a formulation and solution method, called Simulation Sequential Metamodeling (SSM), to facilitate the determination of the optimal vehicle fleet size that minimizes the vehicle cost while satisfying time constraints. The proposed approach is to sequentially construct a series of metamodels, solve the approximate problem, and evaluate the quality of the resulting solution. Once the resulting solution is satisfactory, the algorithm is terminated. Compared with the existing metamodeling approaches that employ a large number of observations for one time, the sequential nature of SSM allows it to achieve much better computational efficiency. Furthermore, a newly developed estimation method enables SSM to quantify the quality of the resulting solution. Extensive numerical experiments show that SSM outperforms the existing methods and the computational advantage of SSM is increasing with the problem size and the level of the variance of response variables. An empirical study based on real data is conducted to validate the viability of SSM in practical settings.

Dynamically reconfigurable entropy coder for multi-standard video adaptation using FaRM

Dynamic and Partial Reconfiguration (DPR) is a feature present in modern Xilinx FPGAs, bringing flexibility to a whole new level. However, it is not yet wide spread in the industry because of poor performance and a lack of a cost model to estimate a solution early in the design process. In this paper, we present our methodology for developing systems capable of dynamic and partial reconfiguration with strict real-time constraints. Our approach is based on FaRM (Fast Reconfiguration Manager), a high-speed controller reaching the configuration port theoretical throughput in Xilinx FPGAs. FaRM performance is estimated using a cost model, which allows us to determine the optimum FIFO size to satisfy timing constraints with the best resources trade-off. We validate our approach with a video application that should be able to encode an H.264 (HD) and an MPEG-2 (SD) stream at the same time. For this we used two entropy encoders on the same reconfigurable zone, while satisfying constraints determined by the video streams. This is the first step of a fully reconfigurable video adaptation system. We also present our unified reconfigurable zone interfaces, specific to video adaptation.

2011 ACM TODAES best paper award

In high-level synthesis for real-time embedded systems using heterogeneous functional units (FUs), it is critical to select the best FU type for each task. However, some tasks may not have fixed execution times. This article models each varied execution time as a probabilistic random variable and solves the heterogeneous assignment with probability (HAP) problem. The solution of the HAP problem assigns a proper FU type to each task such that the total cost is minimized while the timing constraint is satisfied with a guaranteed confidence probability. The solutions to the HAP problem are useful for both hard real-time and soft real-time systems. Optimal algorithms are proposed to find the optimal solutions for the HAP problem when the input is a tree or a simple path. Two other algorithms, one is optimal and the other is near-optimal heuristic, are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, our algorithms achieve an average reduction of 33.0% on total cost with 0.90 confidence probability satisfying timing constraints compared with the previous work using the worst-case scenario.