Developing Online Collaborative Games for e-Learning Environments
Based on our experience, we believe that games, competition and teamwork offer a pleasant and active way of learning. This is much more efficient when the learner has a smile on his face, when he is astonished and curious about next levels and finds the game sufficiently challenging and fun to try again. Our application proposal has the purpose of implementing an e-Learning platform for improving the teaching and learning process in somewhat abstract domains, such as computer architecture or object oriented programming, with the help of games. These games are time-dependent and are able to support collaboration between groups. To this date there are two learning games implemented: a crosswo…
Robust Assembly Assistance Using Informed Tree Search with Markov Chains
Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment partici…
Assembly Assistance System with Decision Trees and Ensemble Learning
This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared …
Multi-objective optimisations for a superscalar architecture with selective value prediction
This work extends an earlier manual design space ex ploration of our developed Selective Load Value Pre diction based superscalar architecture to the L2 unified cache. A fter that we perform an automatic design space expl oration using a special developed software tool by varying several architectural parameters. Our goal is to find optim al configurations in terms of CPI (Cycles per Instruction) and energy consumption. By varying 19 architectural parameter s, as we proposed, the design space is over 2.5 millions of billions configurations which obviously means that only heuristic search can be considered. Therefore, we propose dif ferent methods of automatic design space exploratio n based…
A Visual Simulation Framework For Simultaneous Multithreading Architectures
The computing systems, and particularly microarchitectures, are in a continuous expansion reaching an unmanageable complexity by the human mind. In order to understand and control this expansion, researchers need to design and implement larger and more complex systems’ simulators. In the current paradigm the simulators play the key role in going further, by translating all complex processing mechanisms in relevant and easy to understand information. This paper aims to make a suggestive description of the concepts and principles implemented into a Simultaneous Multithreading Architecture. We introduce the SMTAHSim framework, an educational tool that simulates in an interactive manner the imp…
Forecasting Electricity Consumption and Production in Smart Homes through Statistical Methods
Abstract Over the last years, a steady increase in both domestic electricity consumption and in the adoption of personal clean energy production systems has been observed worldwide. By analyzing energy consumption and production on photovoltaic panels mounted in a house, this work focuses on finding patterns in electrical energy consumption and devising a predictive model. Our goal is to find an accurate method to predict electrical energy consumption and production. Being able to anticipate how consumers will use energy in the near future, homeowners, companies and governments may optimize their behavior and the import and export of electricity. We evaluated the ARIMA and TBATS statistical…
E-learning approach of the graph coloring problem applied to register allocation in embedded systems
The main aim of this paper consists in developing an effective e-learning tool, focused on evolutionary algorithms, in order to solve the graph coloring problem. Subsidiary, we apply graph coloring for register allocation in embedded systems. From didactic viewpoint, our tool has benefits in the learning process because it helps students to observe the relationship between the graph coloring problem and CPU registers allocation with the help of four developed modules: the genetic algorithm, the graphical viewer, the interference graph for a C program and a web application which collects the simulation results. All these applications are combined by a graphical interface which allows the use…
Understanding Prediction Limits Through Unbiased Branches
The majority of currently available branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches which are difficult-to-predict. In this paper, we quantify and evaluate the impact of unbiased branches and show that any gain in prediction accuracy is proportional to the frequency of unbiased branches. By using the SPECcpu2000 integer benchmarks we show that there are a significant proportion of unbiased branches which severely impact on prediction accuracy (averaging between 6% and 24% depending on the prediction context used).
Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions
This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are analyzed for this purpose. Several prediction methods have been previously evaluated and the prediction by partial matching, which was the most efficient, is considered in this work as a component of a hybrid model together with an optimally configured hidden Markov model. The experimental results show that the hidden Markov model is a viable choice to predict the next assembly step, whereas the hybrid predictor is even better, outperforming in so…
Using Two-Level Context-Based Predictors for Assembly Assistance in Smart Factories
The paper presents some preliminary results in engineering a context-aware assistive system for manual assembly tasks. It employs context-based predictors to suggest the next steps during the manufacturing process and is based on data collected from experiments with trainees in assembling a tablet. We were interested in finding correlations between the characteristics of the workers and the way they prefer to assemble the tablet. A certain predictor is then trained with correct assembly styles extracted from the collected data and assessed against the whole dataset. Thus, we found the predictor that best matches the assembly preferences.
Assembly support systems with Markov predictors
In this paper, we analyse Markov prediction as a suitable model to suggest the next assembly step in the manufacturing process. The goal is a decision support system which can assist the workers in...
The Impact of Java Applications at Microarchitectural Level from Branch Prediction Perspective
The portability, the object-oriented and distributed programming models, multithreading support and automatic garbage collection are features that make Java very attractive for application developers. The main goal of this paper consists in pointing out the impact of Java applications at microarchitectural level from two perspectives: unbiased branches and indirect jumps/calls, such branches limiting the ceiling of dynamic branch prediction and causing significant performance degradation. Therefore, accurately predicting this kind of branches remains an open problem. The simulation part of the paper mainly refers to determining the context length influence on the percentage of unbiased bran…
Engineering a Digital Twin for Manual Assembling
The paper synthesizes our preliminary work on developing a digital twin, with learning capabilities, for a system that includes cyber, physical, and social components. The system is an industrial workstation for manual assembly tasks that uses several machine learning models implemented as microservices in a hybrid architecture, a combination between the orchestrated and the event stream approaches. These models have either similar objectives but context-dependent performance, or matching functionalities when the results are fused to support real-life decisions. Some of the models are descriptive but easy to transform in inductive models with extra tuning effort, while others are purely ind…
Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components
This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real…
Exploiting selective instruction reuse and value prediction in a superscalar architecture
In our previously published research we discovered some very difficult to predict branches, called unbiased branches. Since the overall performance of modern processors is seriously affected by misprediction recovery, especially these difficult branches represent a source of important performance penalties. Our statistics show that about 28% of branches are dependent on critical Load instructions. Moreover, 5.61% of branches are unbiased and depend on critical Loads, too. In the same way, about 21% of branches depend on MUL/DIV instructions whereas 3.76% are unbiased and depend on MUL/DIV instructions. These dependences involve high-penalty mispredictions becoming serious performance obstac…
Using FOCAP tool for teaching microarchitecture simulation and optimization
This paper presents our new developed FOCAP tool (Framework for optimizing the Computer Architecture Performance) in order to gain a better understanding and familiarity of the students with new advanced learning methods and tools in the Microarchitecture Simulation and Optimization. At this stage, FOCAP allows a mono-objective automatic design space exploration (DSE) of a superscalar processor by varying several architectural parameters. Such DSE tools are very useful, since it is impossible to simulate all the configurations of a highly parameterized microarchitecture. Therefore, heuristic methods, local search algorithms and advanced machine learning methods are good candidates to find n…
Prediction-Based Assembly Assistance System
This paper presents the design of a prediction-based assembly assistance system for manual operations and the results obtained on the data collected from experiments of assembling a customizable product. We integrated into the proposed system a Markov predictor improved with a padding mechanism whose role is to recommend the next assembly step and to detect the worker’s errors. The predictor is trained with correct assembly patterns and tested with real assembly/manufacturing data. The proposed predictor improves the coverage and, thus, there is a significantly higher number of assembly steps which are correctly correlated with the real intentions of the workers.
Enhancing the Sniper Simulator with Thermal Measurement
This paper presents the enhancement of the Sniper multicore / manycore simulator with thermal measurement possibilities using the HotSpot simulator. We present a plugin that interacts with Sniper to retrieve simulation data (integration areas and power consumptions) and calls HotSpot to compute the corresponding thermal results. The plugin also builds a two dimensional floorplan for the simulated microarchitecture. Furthermore we plan to integrate the simulation methodology presented here into an automatic design space exploration process using the multi-objective optimization tool called FADSE. Keywords—multicore; simulator; power consumption; thermal; HotSpot; Sniper
Performance and energy optimisation in CPUs through fuzzy knowledge representation
Abstract This paper presents an automatic design space exploration using processor design knowledge for the multi-objective optimisation of a superscalar microarchitecture enhanced with selective load value prediction (SLVP). We introduced new important SLVP parameters and determined their influence regarding performance, energy consumption, and thermal dissipation. We significantly enlarged initial processor design knowledge expressed through fuzzy rules and we analysed its role in the process of automatic design space exploration. The proposed fuzzy rules improve the diversity and quality of solutions, and the convergence speed of the design space exploration process. Experiments show tha…
Unbiased Branches: An Open Problem
The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Since our focus is on the impact that unbiased branches have on processor performance, timing issues and hardware costs are out of scope of this investigation. Our simulation results, with the SPEC2000 in…
A study on forecasting electricity production and consumption in smart cities and factories
Abstract The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity producti…
Assembly Process Modeling Through Long Short-Term Memory
This paper studies Long Short-Term Memory as a component of an adaptive assembly assistance system suggesting the next manufacturing step. The final goal is an assistive system able to help the inexperienced workers in their training stage or even experienced workers who prefer such support in their manufacturing activity. In contrast with the earlier analyzed context-based techniques, Long Short-Term Memory can be applied in unknown scenarios. The evaluation was performed on the data collected previously in an experiment with 68 participants assembling as target product a customizable modular tablet. We are interested in identifying the most accurate method of next assembly step prediction…
Towards an Assembly Support System with Dynamic Bayesian Network
Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bay…
Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks
Abstract This paper presents a forecasting method of the electricity consumption and production in a household equipped with photovoltaic panels and a smart energy management system. The prediction is performed with a Long Short-Term Memory recurrent neural network. The datasets collected during five months in a household are used for the evaluations. The recurrent neural network is configured optimally to reduce the forecasting errors. The results show that the proposed method outperforms an earlier developed Multi-Layer Perceptron, as well as the Autoregressive Integrated Moving Average statistical forecasting algorithm.