对称的n/p Schottky屏障调制用于精确配置的神经网络
Miao Zhang1, Moufu Kong1, Yi Cui1
1State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 611731, China.
这项研究引入了一种用于神经形态计算的新型Schottky屏障神经istor,在边缘设备中实现了高精度和高效率. 新设备集成线性和非线性操作,显著减少硬件复杂性和提高性能.
科学领域:
- 神经形态计算是一种神经形态计算.
- 固态设备物理 固态设备物理
背景情况:
- 对于边缘设备而言,传统的神经元阻尼器在平衡计算精度和能源效率方面存在局限性.
- 传统设备的固定精度需要在精度和功耗之间进行权衡.
研究的目的:
- 开发一种单个神经电阻装置,能够同时实现高效非线性逻辑和高精度线性运算,用于边缘计算.
- 为了克服当前神经形态硬件固有的精度效率权衡.
主要方法:
- 提出了一个Schottky屏障神经电阻器,具有全球底门来调节Schottky屏障,确保线性门电压-透导关系.
- 利用静电兴奋剂和图像力效应,实现统一的n/p型调制和增强驾驶能力.
- 模拟了基于拟议的神经电阻器的加速器架构.
主要成果:
- 施托基屏障神经电阻器成功地将线性和非线性功能集成到一个设备中.
- 在模拟中获得了98.3%的准确性和1359.62 TOPS/W的能效.
- 与基于的解决方案相比,非线性函数的设备数量显著减少 (五分之一).
结论:
- 拟议的神经电阻设计使可重新配置的数字-模拟计算单元用于先进的边缘人工智能应用.
- 这一创新为更高效,更精确的神经形态系统铺平了道路.
- 该设备为下一代低功耗,高性能边缘计算提供了一个有前途的解决方案.
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