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相关概念视频

Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
215
Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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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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Classification of Neurotransmitters01:30

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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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相关实验视频

Updated: Jul 21, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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现场可编程门阵列平台的实施,用于使用基于尖端的反向传播的深度卷积尖端神经网络进行对象分类任务.

Vijay Kakani1, Xingyou Li2, Xuenan Cui3

  • 1Integrated System Engineering, Inha University, 100 Inharo, Nam-gu, Incheon 22212, Republic of Korea.

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概括
此摘要是机器生成的。

这项研究比较了两种基于尖峰的反向传播方法,用于在现场可编程门数组 (FPGA) 上训练深度卷积尖峰神经网络 (DCSNNs). 结果指导了对象分类任务的高效,低功耗AI的部署.

关键词:
现场可编程的门阵列.神经形态图像处理神经形态图像处理对象分类性能表现的表现.尖的神经网络的神经网络.

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科学领域:

  • 人工智能的人工智能
  • 神经形态工程的神经形态工程
  • 计算机视觉 计算机视觉

背景情况:

  • 深度卷积尖端神经网络 (DCSNNs) 为边缘AI应用提供了一个有前途的低功耗替代方案.
  • 有效培训DCSNN,特别是在FPGA等硬件平台上,仍然是一个重大的研究挑战.
  • 基于尖的反向传播技术正在成为训练复杂SNN架构的可行方法.

研究的目的:

  • 通过逆向传播 (TSSL-BP) 和通过逆向传播 (SGD-BP) 替代梯度下降来训练DCSNN的有效性.
  • 评估用于对象分类的低功耗现场可编程门数组 (FPGA) 上部署训练有素的 DCSNN 的性能和可行性.
  • 为研究人员和工业提供关于DCSNN在FPGA上部署DCSNN的局限性和优势的见解.

主要方法:

  • 实现并比较TSSL-BP和SGD-BP用于训练使用卷积过器的DCSNN.
  • 开发了一个低功耗的FPGA板,用于部署DCSNNs.
  • 在FPGA上推断了TSSL-BP和SGD-BP模型,用于使用公共 (MNIST,CIFAR10,KITTI) 和私人数据集 (INHA_ADAS,INHA_KLP) 的对象分类.

主要成果:

  • 在各种数据集和网络架构中对TSSL-BP和SGD-BP性能进行比较分析.
  • 在FPGA平台上评估DCSNN推理准确性和效率.
  • 评估FPGA部署的电力消耗和配置要求.

结论:

  • 确定了最有效的基于尖峰的反向传播技术,用于在FPGA上更深的DCSNN.
  • 证明了在低功率FPGA上部署训练有素的DCSNN用于对象分类的可行性.
  • 为优化DCSNN用于边缘AI硬件提供了实际考虑.