为二元神经网络提供单体集成补充铁电FET XNOR突触
Junghyeon Hwang1, Hongrae Joh1, Chaeheon Kim1
1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea.
ACS applied materials & interfaces
|January 4, 2024
概括
研究人员使用互补的铁电晶体管开发了高密度,准确的非挥发性XNOR突触. 这一突破增强了神经形态计算和人工智能硬件,提高了图像识别和能源效率.
科学领域:
- 神经形态计算和人工智能硬件.
- 先进的半导体设备物理和制造.
背景情况:
- 神经形态计算模仿大脑以获得高效的AI硬件.
- 基于XNOR突触的二元神经网络 (BNNs) 提供了紧的尺寸和低成本.
- 现有的XNOR突触面临细胞密度和精度之间的权衡.
研究的目的:
- 为了开发高密度和精度的非挥发性XNOR突触.
- 为了克服以前的XNOR突触设计的局限性.
- 为人工智能和神经形态系统推进硬件实现.
主要方法:
- 使用单立体堆叠的互补铁电场效应晶体管 (C-FeFET).
- 采用双门配置和独特的操作方案,用于n型铁电TFT.
- 执行了数组级模拟 (512x512子数组) 和系统级分析.
主要成果:
- 通过使用2C-FeFETs,高精度地达到每细胞密度60F2.
- 与其他突触相比,显示出更好的图像识别精度 (MNIST +3.17%,CIFAR-10 +14.07%)
- 展示了高吞吐量 (717.37 GOPS) 和能源效率 (196.7 TOPS/W).
结论:
- 开发的C-FeFET非挥发性XNOR突触提供了更高的密度和精度.
- 这种方法显著提高了AI硬件和神经形态系统的性能.
- 这项技术对高密度内存,内存逻辑和神经网络硬件具有前景.
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