走向无参数的注意力尖端神经网络
Pengfei Sun1, Jibin Wu2, Paul Devos1
1Department of Information Technology, Ghent University, Gent, Belgium.
概括
本研究引入了一个无参数注意力 (PfA) 机制,用于尖端神经网络 (SNN). 在不增加内存负载的情况下,PfA增强了SNN性能和噪声强度,因此非常适合神经形态硬件.
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) 为神经形态硬件提供能源效率和时空建模.
- 注意模块改善了顺序数据的SNN,但增加了内存消耗.
- 参数化注意力的高内存使用阻碍了资源受限的神经形态芯片上的SNN.
研究的目的:
- 为SNN开发一个无参数注意力 (PfA) 机制.
- 为了提高SNN功能表示和性能,而无需额外的内存开销.
- 提高SNN对节能神经形态计算的适用性.
主要方法:
- 引入了一个无参数注意力 (PfA) 机制.
- 将PfA集成到尖端神经元中,以加强特征表示.
- 在各种数据集上评估了PFA-SNN,包括SHD,BAE-TIDIGITS,SSC,DVS-Gesture,DVS-Cifar10,Cifar10和Cifar100.
主要成果:
- 在多个数据集中,PFA-SNN实现了竞争性或优越的分类准确性.
- 拟议的PfA机制在不增加模型参数的情况下提高了性能.
- 与传统的SNN和那些具有参数化注意力的SNN相比,Pfa-SNN显示出更好的噪声稳定性.
结论:
- 无参数注意力 (PfA) 机制有效地提高了SNN的性能和效率.
- 对于记忆受限的神经形态硬件来说,PfA是一个可行的解决方案.
- PfA-SNN代表了对空间时间数据的节能AI的重大进步.
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