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培训后量化,以实现高效的ANN-SNN转换
Ruimin Sun1, De Ma2, Gang Pan2
1College of Computer Science and Technology, Zhejiang University, Hangzhou, 310000, China.
概括
通过转换人工神经网络 (ANN),可以更有效地训练尖端神经网络 (SNN). 这项研究表明,道智能值和培训后量化可以减少转换错误,提高SNN准确性和减少培训时间.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 尖端神经网络 (SNN) 模仿生物神经元,以实现高效的计算.
- 目前的SNN培训涉及直接优化或ANN到SNN转换.
- 从ANN转换为SNN通常会出现显著的转换错误.
研究的目的:
- 调查和减轻ANN到SNN转换中的转换错误.
- 提出一种用于训练深度SNN的新方法.
- 提高SNN的准确性和效率.
主要方法:
- 对转换错误的理论分析.
- 实施通道智能值与层智能值.
- 应用培训后量化 (PTQ) 进行有效的校准,而不需要再培训.
主要成果:
- 从理论上讲,通道智能值在减少转换误差方面比层智能值更有效.
- 培训后量化 (PTQ) 可以有效校准SNN.
- 与直接培训和传统的ANN-SNN转换相比,提出的方法显著减少了培训时间.
- 在静态图像和神经形态数据集上提高了准确性.
结论:
- 道智能值和PTQ为准确和高效的ANN-SNN转换提供了有效的策略.
- 这种方法促进了SNN在下一代计算中的实际应用.
- 该方法为直接SNN培训提供了可行的替代方案,提供了较低的计算成本.
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