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Copyright of Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică is the property of National Institute for Research & Development in Informatics - ICI Bucharest and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Functional Verification is one of the main processes in the Research and Development of new System-on-Chip. As chips are becoming more and more complex, this step becomes an extensive bottleneck which can vastly delay the chip mass production. It is a mandatory step as the design needs to not contain any faults, to ensure proper functioning. If this step is bypassed, large major financial losses and customer dissatisfaction can happen later in the process. Additionally, if the verification process is prolonging for a long period of time, to achieve a higher quality product, it will also cause a financial impact. Therefore, the solution is to find ways to optimize this activity. This paper contains a review on how Artificial Intelligence can reduce this blockage, taking into consideration the time spent on implementing the verification environment and the time of attaining the aimed coverage percentage. The engineer will take a decision on which causes of time-consuming processes presented in the paper will be reduced, depending on project specifics and his or her experience. A candidate for optimizing the training of the Neural Network is the Nvidia’s Computer Unified Device Architecture (CUDA). CUDA is parallel computing platform that make use of the GPU, peculiarly of the CUDA cores located inside Nvidia GPUs. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is a field where many researchers have tried to create a model able to recognize emotions from a face. With many applications such as interfaces between human and machine, safety or medical, this field has continued to develop with the increase of processing power. This paper contains a broad description on the psychological aspects of the FER and provides a description on the datasets and algorithms that make the neural networks possible. Then a literature review is performed on the recent studies in the facial emotion recognition detailing the methods and algorithms used to improve the capabilities of systems using machine learning. Each interesting aspect of the studies are discussed to highlight the novelty and related concepts and strategies that make the recognition attain a good accuracy. In addition, challenges related to machine learning were discussed, such as overfitting, possible causes and solutions and challenges related to the dataset such as expression unrelated discrepancy such as head orientation, illumination, dataset class bias. Those aspects are discussed in detail, as a review was performed with the difficulties that come with using deep neural networks serving as a guideline to the advancement domain. Finally, those challenges offer an insight in what possible future directions can be taken to develop better FER systems. [ABSTRACT FROM AUTHOR]