Table Of Content
Among these models, the PSO-CPU-GPU-SVR model exhibits the shortest training time. Additionally, on datasets BMTWO, BMTHREE, and BMFIVE, the PSO-CPU-GPU-SVR model surpasses both the PSO-SVR and GA-CPU-GPU-SVR models in terms of RMSE, MAPE, and R2. While its accuracy is slightly lower than that of the SSA-CPU-GPU-SVR model, the difference is not significant. Remarkably, the PSO-CPU-GPU-SVR model has a substantial advantage in training time, being 14.03 to 35.34 times faster than the PSO-SVR model. Compute Unified Device Architecture (CUDA) is a parallel computing architecture based on the graphics processing unit (GPU) introduced by NVIDIA in 2006, whose internal architecture is shown in Fig.
Supplementary information
The hybrid-paste HPB shows the highest porosity and depicts a different behavior upon sintering. Hyper-parameters can largely affect the generalization ability of the CPU-GPU heterogeneous parallel SVR model. It is difficult to determine the appropriate values of the parameters through a priori knowledge and the process of manually tuning the parameters is very time-consuming. To make the model evaluation more accurate and credible, the particle swarm optimization algorithm was used to find the optimal values for the C and ฯ parameters in Eqs. The steps for parameter optimization of the CPU-GPU heterogeneous parallel SVR model using the particle swarm algorithm are as follows. The main parameters affecting SVR performance are penalty factor C and the radial basis radius of the RBF kernel function ฯ.
Data availability
C is a general-purpose programming language and is highly suitable for embedded software because it is processor-independent, portable, fast, and offers direct access to memory management. While C is not as low-level as assembly language, it is generally less user-friendly than higher-level languages. Therefore, many companies will choose to use automatic code generation tools to streamline the process of converting the model to code.
What is Model-based Development?
Each row of the matrix has m kernel functions to be computed, with a minimum time complexity of O(mk). The kernel function operation is the most complex and time-consuming part of the SMO algorithm, and the kernel function values of the current two sample data need to be calculated in each iteration. Calculating each element in the kernel function matrix requires only two corresponding data corresponding to it x and xi and no additional sample data. Because there is no correlation between any two different elements in the matrix, this satisfies the conditions for parallelization.
Steps
The LTPS CMOS inverter consumes less power regardless of supply voltage because of its complementary structure. When effectively developed, a model-based design offers an enormous improvement in cost reduction and schedule acceleration by removing otherwise manual and burdensome overhead a documentation-based approach relies upon. We’ve long known the use of models has enormous benefits to bring a design to reality.
Microstructure feature importance and mathematical relationship of microstructural features and electrical behavior
Figure 3d depicts the G-M curvature joint distributions of the copper surfaces for different sinter stages. In the first (QI) and second (QII) quadrants, two tails extend to high mean curvature values. The changes of those tails with temperature indicate the change of the copper particles’ convexity. The copper particles’ local geometries at the lower sinter temperature, display mostly cup-convex surfaces resembling spheroidal structures49, therefore, their Ms are positive. As a result, the emerging tails in QI are getting less pronounced by increasing the temperature for all presented materials. The observed behavior is complemented with an increased number of cup-concave geometries indicated by more pronounced tails in QII.
The data set includes information on pollutants such as PM2.5, PM10, SO2, NO2, CO, and O3 and environmental information such as temperature, humidity, pressure, rainfall and wind speed. Moreover, increasing the VBIAS shifts the voltage transfer curve in the right direction, which can be useful for post-fabrication tuning of the static noise margin, depending on the local VT and variability values10. Figure 2a shows the voltage transfer curves in which VBIAS equals twice VDD for pseudo-CMOS logic. Figure 2b shows the logarithmic power consumption curves with respect to Vin for all logic styles, in which a difference in static power consumption of more than six orders of magnitude is observed. The static power when Vin is logic 0 stems from the leakage of n-type devices because of the VT difference in technologies.
The transistor transfer characteristics for both technologies are shown in Supplementary Fig. The traditional photolithography equipment for those foundries exhibits critical dimensions in the micrometre range, yielding transistors with above-ยตm channel lengths. In 2015, Denise Duncan published an influential article advocating for the use of models for virtual prototyping within the Department of Defense.
Similarly, there’s plenty of grandiose vision of what’s possible with little about what is realistic when it comes to model-based design. This has created substantial confusion and a fair bit of skepticism as to what model-based workflows are capable of. Thus, the following series of blogs is intended to clarify much of this confusion from the perspective of Hiller’s own experience developing model-based workflows.
No really, what is MBD? - ENGINEERING.com
No really, what is MBD?.
Posted: Thu, 22 Feb 2024 08:00:00 GMT [source]
You can automatically generate code from a model and, when software and hardware implementation requirements are included, create test benches for system verification. Code generation saves time and prevents the introduction of manually coded errors. As the product development process goes on, the overall understanding of the system improves and system requirements can be iterated upon. However, all connections must be maintained and up to date for the development teams.
Production will typically entail some form of human or driver in the loop testing in a real world environment. The goal is to test the end-to-end functionality with a human present and ready to take control in case of a safety event. At this stage, companies will evaluate the performance of the model against the performance of the product in the real world.
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