转向切换和融合神经形态计算:具有多神经形态功能的垂直批量异质连接晶体管,用于高效的深度学习
Yi Zou1,2, Di Liu1,2, Xinyan Gan1,2
1Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China.
Advanced materials (Deerfield Beach, Fla.)
|April 24, 2025
概括
研究人员开发了一种新的晶体管,将人工神经网络 (ANN) 和尖端神经网络 (SNN) 统一为人工通用智能 (AGI). 这种突破性的设备可以实现高效的,低功耗的神经形态计算,无需辅助电路.
科学领域:
- 神经形态工程的神经形态工程
- 人工通用智能 人工通用智能
- 材料科学 材料科学 材料科学
背景情况:
- 目前用于人工通用智能 (AGI) 的人工神经网络 (ANN) 和尖端神经网络 (SNN) 架构通常是独立的.
- 对ANN和SNN的融合训练通常需要广泛的辅助电路和外部算法,从而限制了效率.
研究的目的:
- 引入一种能够模拟ANN和SNN计算函数的新型垂直批量异质连接神经形晶体管 (VHNT).
- 为高性能,低功耗和自适应性AGI提供统一的设备策略.
主要方法:
- 开发了一种使用基于TaOx的电化学反应进行尖端编码的VHNT,并使用基于PDVT-10/N2200的散装异质连接进行电压编码.
- 证明了设备可编程性,可以在没有辅助电路的情况下在尖端和自我激活的神经元模式之间切换.
- 实现了一个基于VHNT的人工尖端神经网络 (ASNN) 融合模拟架构.
主要成果:
- 该VHNT实现了高效率,每次多次积累 (MAC) 操作的能耗低至0.84nJ,并具有出色的线性.
- 基于VHNT的ASNN融合架构在CIFAR-10数据集上达到95%的准确性.
- 在核聚变模拟中观察到训练速度和整体效率的显著提高.
结论:
- 小说VHNT提供了一种统一和高效的神经形态计算方法,集成ANN和SNN功能.
- 这种设备战略为开发更强大,更节能,更适应的人工智能系统铺平了道路.
- VHNT代表了神经形态硬件的重大进步,减少了对复杂外部电路的依赖.
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