硬件友好的深水库计算是硬件友好的
Claudio Gallicchio1, Miguel C Soriano2
1Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, Pisa, 56127, Italy.
概括
这项研究介绍了一种用于神经形态硬件的新型深层水库计算 (RC) 架构. 硬件友好的设计在非线性计算和短期内存任务上表现出强的性能.
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.
- 硬件实现 硬件实现
背景情况:
- 储计算 (RC) 模型使用固定的循环储来模拟动态循环神经网络.
- 现有的RC模型在神经形态硬件上高效实施时面临挑战.
- 需要硬件友好的深度RC架构来完成复杂的学习任务.
研究的目的:
- 介绍一个新的深水库计算神经网络设计.
- 为神经形态硬件实现开发一个适合神经形态硬件实现的战略.
- 在储更新中解决硬件友好的非线性和噪声建模.
主要方法:
- 提出了一个多层次的深度RC架构,具有环水库拓.
- 实施一对一的水库间连接.
- 嵌入硬件友好的非线性和噪声建模到储库更新方程中.
主要成果:
- 在电子硬件上展示了硬件友好的深度RC架构.
- 展示了对非线性计算和短期记忆任务的有希望的处理能力.
- 与现有的RC系统相比,在时间序列分类任务上取得了竞争性表现.
结论:
- 拟议的深度RC架构为硬件友好的环境提供了优势.
- 该设计对于需要非线性计算和内存的机器学习应用程序是有效的.
- 突出了深度,硬件优化的RC对未来AI系统的潜力.
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