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相关概念视频

Parallel Processing01:20

Parallel Processing

179
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...
179
Multimachine Stability01:25

Multimachine Stability

188
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
188
Acceleration Vectors01:30

Acceleration Vectors

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In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
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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.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

667
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
667
Accelerators01:17

Accelerators

93
Accelerators in concrete serve as admixtures to speed up the hardening process, enabling the concrete to achieve early strength faster. Although accelerators do not necessarily impact the time it takes concrete to set, they reduce this time in practice. A common accelerator is calcium chloride, which is particularly useful for hastening early strength development in cold weather or for rapid repair jobs that require quick heat generation after mixing.
The effectiveness of calcium chloride can...
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相关实验视频

Updated: Jul 18, 2025

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
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Published on: April 26, 2018

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在SpiNNaker 2上高效的SNN多核MAC阵列加速.

Jiaxin Huang1, Florian Kelber2, Bernhard Vogginger2

  • 1Infineon Technologies Dresden, Dresden, Germany.

Frontiers in neuroscience
|August 23, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了使用SpiNNaker 2的MAC数组进行神经网络 (SNN) 的平行加速算法. 这些新的算法显著减少了SNN推理的内存足迹和执行时间.

关键词:
这是一个 MAC 阵列 MAC 阵列.在SNN中,SNN是SNN.在 SpGEMMM 中使用.在SpiNNaker 2中,你可能会看到SpiNNaker 2.多核负载均衡部署部署多核负载均衡部署

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Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
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科学领域:

  • 神经形态计算是一种神经形态计算.
  • 人工智能的人工智能是人工智能.
  • 计算机架构 计算机架构

背景情况:

  • 尖端神经网络 (SNN) 提供低能耗计算,但在大型模型中面临时间挑战.
  • 当前基于CPU的SNN处理对于广泛的数据集和复杂的架构来说是缓慢的.
  • 有效的硬件加速对于实现SNN潜力至关重要.

研究的目的:

  • 在SpiNNaker 2上引入SNN推理的并行加速算法.
  • 调查MAC数组在处理元件 (PE) 中的集成,以提高SNN计算.
  • 开发和评估用于空间时间负载平衡和性能优化的新算法.

主要方法:

  • 将MAC阵列架构集成到SpiNNaker 2的处理元件中.
  • 基于单核优化技术开发并行加速算法.
  • 实施Echelon Reorder模型的信息密集算法.
  • 调整多核二阶段分割和授权部署策略.
  • 在各种SNN模型中进行基准测试,包括现实世界的应用和神经科学模型.

主要成果:

  • 梯级优化算法在测试的SNN模型上实现了显著的内存足迹减少 (74.28%和85.78%).
  • 执行时间大幅缩短,占序列ARM基线的≤24.56%.
  • 证明了有效的时空负载平衡和优化性能.
  • 该研究证实了稀疏矩阵-矩阵乘法 (SpGEMM) 优化对SNN的适用性.

结论:

  • 拟议的并行算法和MAC数组集成为SNN推理提供了高效的加速.
  • 介绍了针对SNN和MAC数组量身定制的新型SpGEMM优化算法.
  • 这项工作将SpGEMM的应用扩展到SNNs,增强神经形态硬件性能.