A Framework for Evaluation of Debug Path Performance in SoC

본 논문은 MPI 집합 통신 함수가 처리 레벨 (transaction level) 에서 변환된다는 가정 하에 MPI 집합 통신 중 방송 (Broadcast), 확산 (Scatter), 취합 (Gather) 함수를 최적화한 알고리즘을 제안하였다. 또한 제안하는 알고리즘이 구동되는 MPI 전용 하드웨어 엔진을 설계하였으며, 이를 OCC-MPE (Optimized Collective Communication - Message Passing Engine) 라 명명하였다. OCC-MPE는 표준 송신 모드 (standard send mode)로 점대점 통신 (point-to-point communication) 을 하며, 집합 통신 중 가장 빈번하게 사용되는 방송, 취합, 확산을 제안하는 알고리즘에 의해 전송 순서를 결정한 후 통신하여 전체 통신 완료 시간을 단축시켰다. 제안한 알고리즘들의 성능을 측정하기 위하여 OCC-MPE를 SystemC 기반의 BFM(Bus Functional Model)을 제작하였다. SystemC 기반의 시뮬레이터를 통한 성능 평가 후에 VerilogHDL을 사용하여 제안하는 OCC-MPE를 포함한 MPSoC (Multi-Processor System on a Chip)를 설계하였다. TSMC 0.18 공정으로 합성한 결과 프로세싱 노드가 4개일 때 각 OCC-MPE가 차지하는 면적은 약 1978.95 이었다. 이는 전체 시스템에서 약 4.15%를 차지하므로 비교적 작은 면적을 차지함을 확인하였다. 본 논문에서 제안하는 OCC-MPE를 MPSoC에 내장하면, 비교적 작은 하드웨어 자원의 추가로 높은 성능향상을 얻을 수 있다. In this paper, on the assumption that MPI collective communication function is converted into a group of point-to-point communication functions in the transaction level, an algorithm that optimizes broadcast, scatter and gather function among MPI collective communication is proposed. The MPI hardware engine that operates the proposed algorithm was designed, and it was named the OCC-MPE (Optimized Collective Communication Message Passing Engine). The OCC-MPE operates point-to-point communication by using the standard send mode. The transmission order is arranged according to the algorithm that proposes the most frequently used broadcast, scatter and gather functions among the collective communications, so the whole communication time is reduced. To measure the performance of the proposed algorithm, the OCC-MPE with the Bus Functional Model (BFM) based on SystemC was designed. After evaluating the performance through the BFM based on SystemC, the proposed OCC-MPE is designed by using VerilogHDL. As a result of synthesizing with the TSMC <TEX>$0.18{\mu}m$</TEX>, the gate count of each OCC-MPE is approximately 1978.95 with four processing nodes. That occupies approximately 4.15% in the whole system, which means it takes up a relatively small amount. Improved performance is expected with relatively small amounts of area increase if the OCC-MPE operated by the proposed algorithm is added to the MPSoC (Multi-Processor System on a Chip).

the cluster-based multiprocessor system-on-chip (MPSoC), which adopts the hybrid interconnection composed of both bus-based and NoC architecture, is a new infrastructure for MPSoC. For obtaining the fast exploration of multiple hardware (HW) and software (SW) implementation alternatives with accurate estimations of performance to tune the MPSoC architecture in an early stage of the design process, this paper use the FPGA device to prototype the cluster-based MPSoC with 17 processing cores. And, a suite of benchmarks, including several parallel applications with different characteristic of parallelism, workload and communication pattern, are designed and presented in this paper. The experiment results show that, the highest speedup ratio is up to 15.850.

Verification has become one of the main bottlenecks in the design process of embedded systems, particularly for Multiprocessor Systems-on-Chip (MPSoCs). Efficiently proving the correctness of a design is of extreme importance to reduce cost and time-to-market. Simulation is a common verification method, but complex systems usually require long simulation times. This work advocates Constraint Programming (CP) as a powerful tool for the verification of performance metrics of MPSoCs. Our methodology was evaluated using streaming applications mapped onto a target MPSoC. The resulting constraint-based scheduling problem allowed us to identify performance constraint violations in a fraction of the time required by simulation-based verification.

Over the years, system on chip (SoC) designs have evolved extensively sophisticated in order to fulfil the need of increasing complexity of running applications driven by the advancement of VLSI technology. The next generation multiprocessor system on chip (MPSoC) integrates hundreds of processing elements on a single chip, expected to achieve high performance, low latency, and low-power consumption. Tiled chip multicore processors (TCMP) with network on chip (NoC) have become a foundation for the computation critical embedded and real-time systems. Faster time-to-market restraint and business competition compelled manufacturers to look for the prospects of manufacturing SoCs integrated with various third party intellectual property (3PIP). Usage of 3PIP gave rise to exploit the underlying interconnect while adding some unwanted malicious circuit known as hardware trojan (HT), making NoC vulnerable to get attacked. A tiniest manipulation of any communication attribute by HT can degrade the overall behaviour of the system significantly while impacting NoC performance metrics. In this paper, we present one of such study considering HT malign behaviour of manipulating the output port of each incoming flit from specific ports once genuine route computation takes place and re-directs all to one port causing disruption in tile communication.The proposed HT is intermittent in nature and activates for few cycles. We study the behaviour of proposed confluence attack and analyze its impact over network level. The empirical evaluation exhibits the misbehaviour of the packet communication in terms of wrong usage of VCs and extra hop distance overhead. To validate the proposed work, various performance metrics like buffer utilization, virtual channel utilization, number of flits processed, link utilization, and the like are analyzed. We also show that such HTs are difficult to detect due to marginal increase at malicious port traffic leaving no trace of malicious conduct.

Performance evaluation is a key task in computing and communication systems. Benchmarking is one of the most common techniques for evaluation purposes, where the performance of a set of representative applications is used to infer system responsiveness in a general usage scenario. Unfortunately, most benchmarking suites are limited to a reduced number of applications, and in some cases, rigid execution configurations. This makes it hard to extrapolate performance metrics for a general-purpose architecture, supposed to have a multi-year lifecycle, running dissimilar applications concurrently. The main culprit of this situation is that current benchmark-derived metrics lack generality, statistical soundness and fail to represent general-purpose environments. Previous attempts to overcome these limitations through random app mixes significantly increase computational cost (workload population shoots up), making the evaluation process barely affordable. To circumvent this problem, in this article we present a more elaborate performance evaluation methodology named BenchCast. Our proposal provides more representative performance metrics, but with a drastic reduction of computational cost, limiting app execution to a small and representative fraction marked through code annotation. Thanks to this labeling and making use of synchronization techniques, we generate heterogeneous workloads where every app runs simultaneously inside its Region Of Interest, making a few execution seconds highly representative of full application execution.

Modern embedded applications keep getting more complex, while battery-powered platforms still face strict energy limits. This makes it crucial to design energy-efficient task mapping strategies for Network-on-Chip (NoC)-based heterogeneous Multi-Processor System-on-Chip (MPSoCs). To address this, we present a novel discrete optimization technique called the Binary Chimp Optimization Algorithm (BChOA). It is built to cut energy use when mapping multiple application tasks onto heterogeneous MPSoC architectures. BChOA takes the population behavior from the original Chimp Optimization Algorithm (ChOA) and modifies it to work more effectively with binary problems. It employs a sigmoid-based method to handle the transition from continuous to discrete, enabling it to search through the space of possible task-to-core assignments more effectively. We tested the effectiveness of BChOA by comparing it to three popular optimization methods: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimisation (ACO). We ran all of them on standard application benchmarks like VOPD, MPEG-4, MMS, MWD, and PIP. BChOA ultimately proved to be more energy-efficient across the board. On average, it used 7.73% less energy than GA, 4.50% less than PSO, and 1.71% less than ACO. We also examined where each of these methods excelled and where they fell short, all within the context of task mapping for NoC-based heterogeneous MPSoC systems.

In conventional multiprocessor SoC (MPSoC) design methods, we find two problems: lack of SW code portability and lack of early SW validation. The problems cause a long design cycle. To resolve them, we present a concept of two-layer hardware-dependent software (HdS). The presented HdS consists of hardware abstraction layer to abstract the sub-system architecture and SoC abstraction layer to abstract the global MPSoC architecture. During the exploration of global and sub-system architectures, the application programming interfaces of presented two-layer HdS allow to keep the SW independent from architectural change. The simulation models of two-layer HdS enable to validate the entire system including the SW and HW design early in the design steps. We show the effectiveness of the presented methodology in the MPSoC architecture exploration of an OpenDiVX encoder system design.

In nanometer-scale process technologies, the effects of process variations are observed in Multiprocessor System-on-Chips (MPSoC) in terms of variations in frequencies and leakage powers among the processors on the same chip as well as across different chips of the same design. Traditionally, worst-case values are assumed for these parameters and then a deterministic optimization technique is applied to the MPSoC application under design. We show that such worst-case-based approaches are not optimal with the increasing variation observed at system-level, and instead, statistical approaches should be employed. We consider the problem of simultaneously choosing MPSoC architecture and task allocation for energy optimization under a given performance constraint. Our experimental results on E3S benchmark suite show that the proposed statistical optimization technique can achieve 33.7% improvement on average over conventional worst-case-based techniques and up to 21.7 % improvement over best previously proposed statistical analysis technique.

In this paper we present the architecture and framework for a benchmark suite that has been developed as part of the DeSiDeRaTa project. The benchmark suite is representative of the emerging generation of distributed, mission-critical, real-time control systems that operate in dynamic environments. Systems that operate in such environments may have unknown worst- case scenarios, may have large variances in the sizes of the data and event sets that they process (and thus, have large variances in execution latencies and resource requirements), and may be very difficult to characterize statically, even by time-invariant statistical distributions. The benchmark suite (called DynBench) is useful for evaluation of the Quality of Service (QoS) management and/or Resource Management (RM) services in distributed real-time systems. As such, DynBench includes a set of performance metrics for the evaluation of the QoS and RM technologies in dynamic, distributed real-time systems. The paper demonstrates the successful application of DynBench in evaluation of the DeSiDeRaTa QoS management middleware.

With improved speech understanding technology, many successful working systems have been developed. However, the high degree of complexity and wide variety of design methodology make the performance evaluation and error analysis for such systems very difficult. The different metrics for individual modules such as the word accuracy, spotting rate, language model coverage and slot accuracy are very often helpful, but it is always difficult to select or tune each of the individual modules or determine which module contributed to how much percentage of understanding errors based on such metrics. A new framework for performance evaluation and error analysis for speech understanding systems is proposed based on the comparison with the 'best-matched' references obtained from the word graphs with the target words and tags given. In this framework, all test utterances can be classified based on the error types, and various understanding metrics can be obtained accordingly. Error analysis approaches based on an error plane are then proposed, with which the sources for understanding errors (e.g., poor acoustic recognition, language model, search error, etc.) can be identified for each utterance. Such a framework will be very helpful for design and analysis of speech understanding systems.

La mesure de la performance financière est une question cruciale pour les parties prenantes de l’entreprise que sont les actionnaires, les membres des conseils d’administration, les dirigeants ou les bailleurs de fonds. Par ailleurs, l’efficacité de plusieurs systèmes de gestion (l’évaluation de la performance, la rémunération) est tributaire d’une sélection de mesures de performance appropriées. Dans le cadre de cette étude, cinq mesures de performance financière sont évaluées quant à leur pertinence pour les marchés boursiers et à leur utilité prédictive en matière de flux de trésorerie (modèle comptable américain) et de valeur ajoutée (modèle comptable européen). Ces mesures sont le résultat net, le résultat d’exploitation, le flux de trésorerie d’exploitation, le résultat résiduel et la valeur ajoutée. L’échantillon regroupe des firmes de trois pays dont le processus de normalisation de la comptabilité diffère : la France, les États-Unis et la Suisse. Il se dégage des résultats que la pertinence et l’utilité prédictive des mesures de performance financière dépendent des règles régissant le fonctionnement des marchés financiers et la normalisation comptable dans chaque pays. Ainsi, un processus de normalisation comptable plus souple orienté vers les besoins des investisseurs génère une relation plus étroite entre les mesures de performance et les rendements boursiers. En revanche, pour prédire les flux de trésorerie, les mesures de performance doivent reposer sur des normes comptables qui favorisent la précision et qui laissent peu de place à la flexibilité (mesures fiables). Une trop grande latitude dans l’application des règles comptables peut réduire considérablement l’utilité prédictive des mesures de performance. En outre, pour prédire la valeur ajoutée, une mesure macro-économique, le cadre français est nettement plus performant que le cadre suisse ou américain. Enfin, le fait de respecter ou non les normes IAS n’a qu’un impact mineur sur la pertinence et l’utilité prédictive des mesures de performance. Toutefois, ces résultats sont tributaires des modes de calcul utilisés pour les différents indicateurs de performance et du fait que les règles en matière de comptes consolidés pour les firmes françaises peuvent différer des règles régissant les comptes individuels.

A Public Private Partnership (PPP) describes an arrangement between the public and the private sectors for providing a public asset or service. PPPs are characterized by their long-term nature, complex contractual agreements, and distinct risk allocation formulas, all which distinguish them from traditional procurement routes. Performance evaluation is a prerequisite of performance improvement and is vital to the realization of project success, particularly in PPP projects. In fact, the absence of an effective performance evaluation framework in PPPs serves as a trigger for generating below optimum service quality. Although several performance evaluation frameworks exist, they prove more suitable for traditional projects, which highlights the need to account for the unique features of PPPs. This paper first introduces the various perspectives, from the literature, used to classify project success and evaluate performance. The paper then addresses the aforementioned need by introducing a new framework for evaluating project success, using the lean concepts of transformation, flow, and value. Key Performance Indicators (KPIs) are suggested as part of the framework to serve as performance measures on PPP projects. The introduced lean framework is the first of its kind and fills the gap in PPP research by being one of the few performance evaluation tools customized for PPPs.

In this paper, a dynamic system consisting of a robot manipulator and a target is analyzed. The target is considered in a general way as a dynamic subsystem having finite mass and moments of inertia (e.g., a rigid body or a second robot). The situation investigated is when the robot establishes interaction with the target in such a way that it intercepts and captures a reference element of the target. The analysis of target capture is divided into three phases in terms of time: the precapture, “free” motion (finite motion); the transition from free to constrained motion in the vicinity of interception and capture (impulsive motion); and the postcapture, constrained motion (finite motion). The greatest attention is paid to the analysis of the phase of transition, the impulsive motion, and dynamics of the system. Based on the use of impulsive constraints and the Jourdainian formulation of analytical dynamics, a novel approach is proposed for the dynamic modeling of target capture by a robot manipulator. The proposed approach is suitable to handle both finite and impulsive motions in a common analytical framework. Based on the dynamic model developed and using a geometric representation of the system’s dynamics, a detailed analysis and a performance evaluation framework are presented for the phase of transition. Both rigid and structurally flexible models of robots are considered. For the performance evaluation analyses, two main concepts are proposed and corresponding performance measures are derived. These tools may be used in the analysis, design, and control of time-varying robotic systems. The dynamic system of a three-link robot arm capturing a rigid body is used to illustrate the material presented.

Aim: In addition to affecting the individual sociologically and psychologically, heart disease also poses important problems in health systems. Evaluation of heart disease performances has gained great importance in terms of machine learning method. In the study, performances were compared with the machine learning method for risk methods that classify heart illness. Materials and Methods: The categorization process Throughout the research made use of the "Heart Disease Dataset," an open access dataset. F1-score, sensitivity, selectivity, accuracy, balanced accuracy, negative and positive predictive values were used to assess the performance of the categorisation model using the machine learning approach. Random forest method, one of the variable selection methods, was used. Results: According to the relational classification model's classification findings for heart disease, the accuracy, balanced accuracy, sensitivity, selectivity, positive predictive value, negative predictive value, and F1-score values were observed to be 0.997, 0.997, 0.995, 1, 1, and 0.995, respectively. Conclusion: The relational classification model proposed in the analysis obtained in the web-based open access dataset yielded distinctively successful results in classifying heart disease according to performance criteria.

Purpose This study aims to assess and enhance the production efficiency and reliability of fertilizer manufacturing plants through the application of Markovian analysis. By integrating stochastic modeling with performance management theory, the research provides a predictive performance measurement framework that links reliability metrics to key business outcomes. The research develops a probabilistic framework to model system state transitions, evaluate system availability and optimize maintenance strategies to reduce downtime and improve overall plant performance. Design/methodology/approach A discrete-time Markov chain model is constructed to represent the operational dynamics of critical units within a fertilizer plant. Historical operational and maintenance data are analyzed to develop transition probability matrices that capture the likelihood of state transitions between operational, idle and failure conditions. Key performance metrics such as steady-state probabilities, mean first passage times and recurrence times are computed to assess long-term system behavior. Statistical hypothesis testing – including paired t-tests, Wilcoxon signed-rank tests and chi-square tests – is employed to validate improvements in reliability and efficiency following the implementation of Markov-based maintenance strategies. Regression analysis is also conducted to examine the relationships between operational parameters (e.g. downtime and failure frequency) and production output. Findings The analysis reveals that the production units remain in the operational (RUN) state approximately 61.01% of the time, compared to 53% prior to optimization. Markovian-based maintenance strategies significantly reduced average weekly downtime from 42 h to 29 h (p &amp;lt; 0.001). Weekly production output increased from an average of 1,250 tons to 1,375 tons (p &amp;lt; 0.001). A chi-square test confirmed statistically significant changes in system state transitions (p &amp;lt; 0.001), favoring increased operational continuity. Confidence intervals constructed for key reliability parameters further strengthened the robustness of the findings. Practical implications This study provides a data-driven methodology for improving maintenance planning and production reliability in fertilizer plants. By modeling system behavior through Markovian analysis and applying statistical validation techniques, maintenance managers can develop predictive strategies that reduce unplanned downtime and enhance production efficiency. The methodology is adaptable to other continuous process industries where uptime and reliability are operational priorities and can be integrated into existing performance management systems to support data-driven decision-making and strategic alignment of maintenance activities with productivity goals. Originality/value This study offers a novel academic contribution by applying discrete-time Markov chain modeling to fertilizer manufacturing using empirical operational data. It advances performance management research by integrating stochastic modeling with statistical validation to quantify production efficiency and system reliability. The linkage between probabilistic reliability metrics (e.g. steady-state probabilities and mean first passage times) and business key performance indicators (e.g. downtime and weekly output) provides a new data-driven framework for industrial performance evaluation. This work bridges theoretical modeling with applied maintenance strategies, offering a transferable methodology relevant to researchers and practitioners seeking to optimize reliability and productivity in continuous-process industries.