Wardi tech 2018 download pes android

Seperti kamu juga mendapatkan 3. Didukung dengan penampilan para pemain bola terkemuka dunia, game ini juga menghadirkan kualitas grafis yang lebih menawan. Meski sekilas terlihat sama dengan yang dimiliki oleh PES , tapi grafis ini tampilannya lebih HD dan memiliki sentuhan 3D. Sehingga animasi dari pergerakan permainan, video, dan pemain akan lebih terasa gregetnya selama kamu bermain, geng. Setelah Jaka menjabarkan fitur-fitur yang dimiliki eFootball PES Steam, tentu kamu ingin segera download game ini bukan?

Tenang, Jaka sudah siapkan link download PES di bawah ini. Dijamin free dan bisa langsung kamu gunakan untuk bermain game sepak bola yang seru. Tentunya eFootball mempunyai lebih banyak memberikan tawaran menarik buat kamu yang menggemari game sepak bola satu ini.

Apakah ada lagi game PC yang ingin kalian dapatkan di JalanTikus. Tinggalkan komentarmu di kolom komentar di bawah ini ya. Transaksi Unik! Tentang eFootball PES Download Zona Cacing. Tags Terkait: xbox sepak bola. Gaming 2 jam yang lalu. Tips 3 jam yang lalu. We would be looking at some expected features that the latest PES should have.

In passing of football and in trading of players there will be a great improvement. In goal tactics and technique the pro evolution soccer will be improved. For playing single and multiplayer mode you should improve your Wi-Fi connection. The game should come with an awesome animination and elegant graphics.

The game will come with full access to play the Manager mode. Coming with improved club earning by upgrading stadium. Extract the OBB Data using an xplorer on from your device. This means that the change in power consumption due to voltage and frequency levels occurs at about 20 Hz. Figure 3 shows a sample power consumption trace, profiled while running a graphics benchmark on the GPU.

Figure 3a plots the frequency domain spectrum of the trace before and after filtering. It shows that the effect of high frequency noise and power line noise were significantly reduced. Similarly, Figure 3b shows that, after applying the filter, the time domain signal exhibited much lower variance in amplitude.

However, the filtered signal still had periodic spikes, which were typically caused by the background activity, independent of the workload. A point moving-average, despiking filter removed these spikes, as shown in Figure 3b.

In summary, filtering the power consumption traces ena- bled effective attenuation of the noise in the power consumption traces. The black rectangle shows the major com- ponent of the power. Results 3. Since the display uses LED technology, each pixel can be controlled inde- pendently. The first coefficient represents a bias term while the other coefficients correspond to the respective colors.

The dumpsys command in Android is used to dump the screen and obtain the pixel values at runtime. Since the resolution of the screen is large, the display is sub-sampled both temporally and spatially.

Figure 4 demonstrates the effect of brightness and color on the display power while displaying a solid image of a single color. Power consumption increased with brightness as expected.

Also, it is interesting to note that different colored, red, green, and blue pixels, did not consume the same amount of power. Blue pixels were more power- hungry than the other two, as evident from Figure 4. With the help of these measurements, the unknown coefficients in Equation 2 were obtained using the linear regression method. Figure 5 shows the actual display power and the power predicted by the model for various colors.

The model predicted the power consumption of the display within 0. The average error of the display power model when tested on solid color images was 7. Figure 4. Display power variation with brightness and color. Figure 5. Comparison of actual and predicted display power for different colors. The proposed model was further validated for the complex image shown in Figure 6.

To evaluate the accuracy of the display power model, the brightness of the display was varied as shown in Figure 7. The error is lower in Figure 7 than Figure 5 because it shows the error for test images. That is, the test image is a combination of multiple colors. Note that the learned model overestimated the power consumption of the display with increasing brightness. Overall, the predicted power consumption of the display was within 0. Figure 6. The test image. Electronics , 10, 7 of 29 Figure 7.

Comparison of actual and predicted display power for the test image. To ensure that the measured power consisted of only the big core power, the little core cluster and the GPU were turned off, while the display brightness was reduced to zero.

Furthermore, the device was placed in airplane mode to turn off the network and WiFi radios. During these experiments, the phone stayed idle to ensure that the temperature did not increase due to the dynamic power. Power consumption was meas- ured for 20 s at each temperature while keeping the phone idle. Figure 8 shows the varia- tion in the power consumption, as the temperature was increased, for three different core configurations.

After finding the unknown parameters, the power consumption was found using Equation 5 as a function of the temperature. Electronics , 10, 8 of 29 Figure 8. Power estimation when total power is dominated by leakage of A57 cores. The red curves in Figure 8 show the results of the estimation using the model. The proposed model was able to closely follow the measured power consumption.

The mean squared error for all the three core configurations was less than 0. These values were small compared to the actual power values which are in the order of one watt. In summary, the non-linear regression methodology can estimate the leakage power of the A57 cluster with high accuracy.

Dynamic power model: As the first step to model the dynamic power consumption, the leakage power estimate was substituted for the leakage power in Equation 5. For the big core cluster, the model uses the hardware coun- ters listed in Table 1. The counters include five hardware performance counters and four utilizations. Least squares regression using this model finds the coefficients that fit the performance counter data to the reference dynamic capacitance.

Table 1. CPU feature selection table. Specifically, subset feature selection takes all possible com- binations of the features and trains a model with each subset of features. To derive the dynamic power model for the big cluster, three frequencies in the system were used, i. Three CPU-intensive workloads listed in Table 2 were executed on big cores at each of these frequencies. The power consumption and per- formance counters were recorded during these experiments. Then, the estimate from the leakage power model was subtracted from the total power to find the dynamic power consumption reference.

This reference was used for feature selection and fitting the model in Equation 8 with the best set of features. Figure 9 shows the reference dynamic power consumption and the dynamic power estimated by the model for all three benchmarks running at 1.

The proposed model closely follows the reference power consump- tion. The mean absolute percentage error was only about 6. Table 3 shows a summary of results for all three frequencies. Moreover, the error was minimum for the highest frequency, which is most commonly used in intensive workloads.

Figure 9. Reference and estimated dynamic power consumption for the A57 cluster running at 1. The models presented in this section were used at runtime to estimate the power consumption of the A57 cluster. Table 3 shows that the features selected for each fre- quency of operation were not the same. Since using different features for different fre- quencies can lead to additional overhead at runtime, the union of features in Table 3 were used. The summary of results using the union of features in Table 3 is shown in Table 4.

The average error was similar to or better than the error values in Table 3. Consequently, the union of the features can be used as a single set of features for all the frequencies. Electronics , 10, 10 of 29 Table 2. Benchmarks used in dynamic power estimation and their runtime. Summary of results for the A57 cluster dynamic power estimation.

To estimate the power consumption of the little cluster, the big cores and GPU were turned off. The rest of the modeling used the same methodology as was used for the big CPU cluster.

Therefore, the results for the little cluster are summarized without repeating the steps of the methodology. First, the leakage power parameters for the little core cluster were estimated by re- peating the power measurements using the furnace, while running a light workload on the little CPU cluster.

Using these measurements, the leakage power parameters in Equa- tion 5 were estimated using non-linear curve fitting. Figure 10 shows the power meas- urements at different temperatures for two core configurations.

The first plot shows that the total power consumption increased with temperature as expected. Separation of the dynamic power from the total power consumption showed that it was almost constant at all temperatures. This was expected since the phone was idle when performing the meas- urements. Finally, the rightmost figure shows the variation in the leakage power as the temperature of the phone changed. The measured leakage power consumption was used to identify the leakage power parameters.

The learned parameters were substituted in Equation 5 to compute an estimation of the power consumption. The estimated total power is plotted using a red line in the leftmost figure. The red curves closely follow each other which implies that the estimated power approximates the measured power con- sumption very well.

Next, leakage power was used in the total power model to estimate the dynamic power consumption of the little core cluster. To derive the dynamic power model for the little cluster, the following three frequen- cies were used, i. Three CPU-intensive workloads listed in Table 2 were run on little cores at each of these frequencies. Equation 7 shows the general dynamic power model template. Following a procedure similar to the big CPU cluster, performance counters were fitted to the measured dynamic power consumption.

Figure 11 shows the reference dynamic power and the estimate of the dynamic power. The estimate of the dynamic power follows the trends in the reference power. The MAPE for the estimate was 5. A summary of results for all three fre- quencies is provided in Table 5.

This is mainly because the effect of noise is higher at lower frequencies, thus resulting in a lower signal-to-noise ratio. Due to this, it is difficult to track all the changes in power consumption. Electronics , 10, 11 of 29 Figure The figure shows both measured and estimated power at two configurations.

The dynamic power is constant since the processor is idle. The leakage power shows an increase with temperature due to the temperature term in Equation 5.

Figure Comparison of measured and estimated dynamic power for the A53 cluster running at 1. Each sample is 50 ms. Similar to the big core cluster, the union of features provided a single set of features for all the frequencies.

Table 6 shows the summary of results using the union of features for the little core cluster. The error was similar to the error as was observed for the selected features. Therefore, the union of features can be used as a single set of features for the little core power modeling. Table 4. Summary of results for the A57 cluster with the union of features.

Table 5. Summary of results for A53 dynamic power modeling. Electronics , 10, 12 of 29 Table 6. Summary of results with union of features. Therefore, this section only summarizes the changes required for the GPU power model.

The first step is modeling the leakage power consumption by running a light work- load at different temperatures. Then, the leakage power model is used to obtain the dy- namic power model for the GPU. The rendering test displays a series of cubes on the display. The rate at which the cubes are displayed, and the complex- ity of the cubes can be controlled by the user. This capability allows controlled experi- ments for the GPU power consumption modeling.

Therefore, while performing GPU power modeling, the leakage and dynamic power consumptions of the little CPU cluster are sub- tracted from the total power. Therefore, this section focuses on estimating the other unknowns in Equation 14 , i. To find the unknowns in Equation 14 , the phone was placed in a furnace while run- ning the rendering test benchmark.

As a result, 36 distinct measurements were obtained for the total power consumption. Figure 12 shows the variation of the power consumption with GPU frequency and temperature. As ex- pected, an increase in the power with temperature and frequency was seen. Ta- ble 7 shows the values of the obtained parameters to model leakage power consumption for the GPU. The root mean squared error for the fit was 0.

Variation of power with temperature and frequency. Table 7. Leakage power parameters for the GPU. GPU feature selection table. Using the methodology described in Section 3. At the end of the feature selection process, the set of features that minimized the estimation error were cho- sen. The MAPE at this frequency was 8. This low MAPE shows that the estimated dynamic capaci- tance closely follows the reference dynamic capacitance. Table 9 shows the summary of results for all the frequencies of the GPU.

There- fore, the model can predict the average power of an application with high accuracy. Following the methodology used to model CPU power, the union of features was considered as a single set of features to model the dynamic power consumption of the GPU.

The summary of results using the union of features is also shown in Table The average error with union of features was similar to or better than the error values with selected features. Electronics , 10, 15 of 29 Table 9. Summary of results for the GPU dynamic power model. Summary of results with the union of features for the GPU dynamic power.