基于FET的纳米离子固体电解质储计算,用于高效的时间数据分类和预测
Ankit Gaurav1, Xiaoyao Song2, Sanjeev Kumar Manhas1
1Indian Institute of Technology Roorkee, Roorkee, 247667, India.
ACS applied materials & interfaces
|March 7, 2025
概括
一个新的三端纳米离子固体电解质FET (SE-FET) 增强了边缘系统的储计算. 这项技术改善了时间数据处理,降低了成本,并实现了手写数字分类和混乱时间序列预测的高准确性.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算机科学与工程 计算机科学与工程
- 人工智能的人工智能
背景情况:
- 物理动态储库对于边缘系统是有效的,通过内存计算,通过低培训成本处理时间数据.
- 双终端memristor储存器的局限性包括时间输入持续时间的限制和用于预测的高设备数量,增加复杂性和成本.
- 现有的系统在长期预测方面扎,通常需要增加复杂性的反循环.
研究的目的:
- 开发使用三端纳米离子固体电解质场效应晶体管 (SE-FET) 的高效储计算系统.
- 为了克服传统的两个终端设备的短期内存限制,用于延长时间输入处理.
- 为了提高水库计算效率,并降低边缘应用程序的硬件/培训成本,如分类和预测.
主要方法:
- 使用三端纳米离子固体电解质FET (SE-FET),其中排水电流由门和排水电压调节.
- 实现了单独的控制终端用于读写操作,简化了设计和提高了水库效率.
- 测试了SE-FET储存器用于手写数字分类和混乱的时间序列预测任务.
主要成果:
- 在手写数字分类中获得了95.41%的准确性,用更长的面具长度,每样使用了51%更少的容器输出.
- 使用只有四个无反的SE-FET设备,证明了50个时间步骤的高效长期预测,实现0.06.的低平方根平均误差.
- 在不影响分类准确性的情况下,SE-FET方法显著降低了硬件和培训成本.
结论:
- 三个终端的SE-FET为边缘系统中的水库计算提供了高效和成本效益的解决方案.
- 扩展的短期内存和简化的设计使得在时间数据处理,分类和长期预测方面具有卓越的性能.
- 这项技术比传统的双终端设备有了显著的进步,为更复杂的边缘AI应用铺平了道路.
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