5G networks have arrived as a boon for drastically increasing need for higher data speeds and wider bandwidths. The 5G networks are expected to provide ubiquitous connectivity, higher throughput and very high speed da...
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This paper explores a unique antenna model to allow push-totalk underwater communications. This antenna version uses fuzzy logic to adaptively control the transmission and reception parameters of the antenna. The russ...
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As a critical component of the power system, the operational stability and efficiency of the converter station directly affect the safety and reliability of the entire power system. The existing converter station mana...
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Large Language Model (LLM) training differs significantly from traditional computing tasks, presenting unique challenges for data center design. This computationally intensive workload demands low latency, high throug...
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ISBN:
(纸本)9798350379129;9798350379112
Large Language Model (LLM) training differs significantly from traditional computing tasks, presenting unique challenges for data center design. This computationally intensive workload demands low latency, high throughput, and lossless network operations. We present an analysis of networking solutions designed to address these challenges in Artificial Intelligence (AI) focused data centers. Our approach leverages High Performance computing tools and protocols, including Remote Direct Memory Access (RDMA), InfiniBand (IB), and Priority-based Flow control (PFC). We examine high performance networking solutions such as RoCEv2, GPU Cluster Design, and Rail Optimized Design. These solutions effectively mitigate issues of packet loss and congestion, crucial for LLM training environments. Our analysis reveals potential challenges in implementing these solutions, providing valuable insights for optimizing data center operations in LLM training. This work contributes to the evolving field of AI infrastructure, offering a roadmap for researchers and practitioners developing next generation data centers for advanced AI applications.
Within affective computing, vocal sentiment detection is a new field focusing on speech-based interpretation of human emotions. This work explores cutting-edge expert systems and audio processing techniques to increas...
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This article is devoted to the theoretical study of the properties of discrete optimization problems, as well as methods of their effective implementation on modern multiprocessor computing systems and the experimenta...
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An innovative approach to precision agriculture through the integration of artificial intelligence (AI) and nano-fertilizer technology in an agricultural drone system. The proposed system aims to optimize crop yield, ...
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automation systems for mobile robots have advanced significantly in artificial intelligence, especially in autonomous learning. Nevertheless, prior research has mostly concentrated on predetermined routes, ignoring ob...
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This paper aims at studying how much AI, IoT, and automation play a crucial role in improving the calibration and effectiveness of physical inventory count exercises. As supply chain networks become enhanced, companie...
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The sole objective of the microelectronics industry nowadays is to accommodate many transistors on a single chip, particularly in the nanometer region which is done using scaling. Scaling has given rise to a significa...
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