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The advanced encryption standard (AES) has been widely used to protect digital data in various applications, such as secure IoT communication, files encryption, and pseudorandom number generation. The efficient implementation of AES on parallel architecture, such as graphics processing unit (GPU), has attracted considerable interest over the past decade. These prior studies mainly implemented the AES electronics code book (ECB) and counter (CTR) mode using the table-based approach. In this brief, we set a new speed record of AES-ECB and AES-CTR on GPU based on the proposed bit-sliced implementation techniques. Our implementation achieved 2.6% (ECB) and 9% (CTR) faster than the state-of-the-art table-based implementation on a RTX3080 GPU. Our work evaluated on an embedded GPU (Jetson Orin Nano) can also achieve high throughput at 60 Gb/s, which is 1.9% (ECB) and 7% (CTR) faster than state-of-the-art. [ABSTRACT FROM AUTHOR]

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe. [ABSTRACT FROM AUTHOR]