通过在ANN中显式建模残余错误来转换高性能和低延迟SNN
概括
本研究引入了一种新的方法来改进低延迟边缘设备的尖端神经网络 (SNN). 通过将残余错误建模为噪声,该方法可以提高转换为SNN的人工神经网络 (ANN) 的精度和效率.
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.
背景情况:
- 尖端神经网络 (SNN) 在神经形态硬件上提供能源效率和有效性.
- 人工神经网络 (ANN) 转换为SNN利用SNN培训带来SNN好处.
- 现有的转换方法在低延迟条件下与剩余错误作斗争,限制了边缘设备上的SNN.
研究的目的:
- 为了解决在超低延迟条件下的残余错误引起的ANN-SNN转换中的性能差距.
- 提出一种与神经形态芯片兼容的新型转换方法.
- 提高SNNs的准确性和效率,用于对延迟敏感的边缘应用.
主要方法:
- 显式建模的残余错误作为添加噪声.
- 在源的激活函数中内置的噪音 ANN.
- 对CIFAR10/100和Tiny-ImageNet数据集的方法进行了评估.
主要成果:
- 拟议的方法显著减少了残余错误对SNN性能的影响.
- 与现有的ANN-SNN转换方法相比,实现了更高的精度和更短的时间步骤.
- 在实验中表现优于受过直接训练的SNNs.
结论:
- 这种新的方法为改善SNN在超低延迟约束下性能提供了可行的解决方案.
- 该方法提高了转换后的SNN与神经形态硬件的适应性和兼容性.
- 预计将加速神经形态硬件在边缘计算中的实际应用.
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