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相关概念视频

Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Neural Regulation01:37

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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Neuronal Communication01:28

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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Reducing Line Loss01:18

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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尼龙:最快的方法,有效地在边缘执行您的深度学习算法.

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    此摘要是机器生成的。

    在深度学习 (DL) 模型中,NERONE可实现无的现场可编程门阵列 (FPGA) 加速,提高能源效率而不会改变开发工作流程. 与临床应用中的图形处理单元 (GPU) 相比,这种解决方案可节省大量的电力.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 硬件加速器 硬件加速器

    背景情况:

    • 深度学习 (DL) 模型对于临床应用至关重要,例如辐射剂量定量和手术规划.
    • 图形处理单元 (GPU) 常用于DL推断,但耗电密集,限制其在受限制环境中的使用.
    • 现场可编程门阵列 (FPGA) 提供优越的每瓦性能,但对于非专家来说,使用它们是具有挑战性的.

    研究的目的:

    • 推出NERONE,这是一种简化DL模型FPGA加速的工具.
    • 为了使最终用户能够在不改变现有的DL开发流程的情况下利用FPGA的能源效率.
    • 为了证明NERONE在不同网络架构和FPGA板上的多功能性.

    主要方法:

    • 为各种数据集开发了四种DL模型 (三个用于细分,一个用于分类).
    • 在使用NERONE框架的嵌入式FPGA板上部署这些模型.
    • 与移动和数据中心GPU相比,评估了能源效率的改进.

    主要成果:

    • NERONE促进了FPGAs用于DL推断的使用,使它们能够被更广泛的受众访问.
    • 与移动GPU相比,平均能效提高了3.4倍.
    • 与数据中心GPU相比,平均能效提高1.9倍.

    结论:

    • NERONE有效地弥合了DL开发和FPGA硬件加速之间的差距.
    • 该工具提高了临床DL应用的能源效率,特别是在功率受限的环境中.
    • NERONE支持各种网络架构,展示了其适应性和广泛适用性.