Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Optimization Problems01:26

Optimization Problems

102
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
102
Improving Translational Accuracy02:07

Improving Translational Accuracy

15.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.7K
3.7K
Parallel Processing01:20

Parallel Processing

819
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...
819
Neural Circuits01:25

Neural Circuits

3.0K
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...
3.0K
Reducing Line Loss01:18

Reducing Line Loss

406
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 in...
406

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration.

IEEE micro·2023
Same author

ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks.

IEEE micro·2021
Same author

Step-economical synthesis of 3-amido-2-quinolones by dendritic copper powder-mediated one-pot reaction.

Organic & biomolecular chemistry·2018
Same author

Clonazepam treatment of pathologic childhood aerophagia with psychological stresses.

Journal of Korean medical science·2007
Same author

Estrogen and enalapril attenuate the development of right ventricular hypertrophy induced by monocrotaline in ovariectomized rats.

Journal of Korean medical science·2003
Same journal

Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How & When to Adapt.

... International Conference on Learning Representations·2026
Same journal

Topology-Aware Segmentation Using Discrete Morse Theory.

... International Conference on Learning Representations·2026
Same journal

TOPODIFFUSIONNET: A TOPOLOGY-AWARE DIFFUSION MODEL.

... International Conference on Learning Representations·2026
Same journal

GEOMETRY OF LONG-TAILED REPRESENTATION LEARNING: REBALANCING FEATURES FOR SKEWED DISTRIBUTIONS.

... International Conference on Learning Representations·2026
Same journal

Probabilistic Geometric Principal Component Analysis with application to neural data.

... International Conference on Learning Representations·2026
Same journal

BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.

... International Conference on Learning Representations·2026
查看所有相关文章

相关实验视频

Updated: Feb 28, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

驼:适应性代码优化用于加速深度神经网络编译.

Byung Hoon Ahn1, Prannoy Pilligundla1, Amir Yazdanbakhsh2

  • 1University of California, San Diego.

... International Conference on Learning Representations
|February 26, 2026
PubMed
概括
此摘要是机器生成的。

驼是一种新的强化学习方法,通过学习自适应抽样策略来加速神经网络代码的优化. 这大大减少了编译时间,并提高了深度网络推理性能.

相关实验视频

Last Updated: Feb 28, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 当前的神经网络执行方法依赖于非最佳的,耗时的技术,如手动优化的库或遗传算法.
  • 这些方法通常涉及频繁,昂贵的硬件测量,阻碍了效率和创新.

研究的目的:

  • 开发一种适应性解决方案,用于神经网络中的快速代码优化.
  • 为了加快寻找最佳代码的速度,并提高未见的设计空间的输出性能.

主要方法:

  • 利用强化学习 (RL) 来实现更快的优化趋同.
  • 开发一个适应性采样算法,优先考虑代表性的硬件测量.
  • 纳入领域知识启发逻辑来提高样本质量.

主要成果:

  • 与AutoTVM相比,Chameleon在优化时间中实现了4.45倍的加快速度.
  • 对于现代深度网络来说,推断时间有5.6%的改善.
  • 成功地适应了以前看不见的设计空间,以实现高效的代码优化.

结论:

  • 驼为神经网络代码优化提供了一种更高效,更有效的方法.
  • 适应性采样和RL集成显著减少了优化时间,提高了性能.
  • 这种方法通过能够更快地代和部署各种神经网络架构来促进创新.