量子拓神经电阻器用于高级神经形态智能系统
Dani S Assi1, Hongli Huang1, Vaithinathan Karthikeyan1
1Electronics and Nanoscale Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|June 21, 2023
概括
量子拓神经晶体管 (QTN) 提供超低能耗和高开关速度,用于先进的神经形态计算. 这些生物灵感设备模仿大脑突触,为下一代智能机器铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 神经形态人工智能 (AI) 系统对于高性能计算至关重要,但如果没有专门的设备设计,则面临缓慢的进步.
- 模仿哺乳动物大脑突触是开发高效神经形态系统的关键.
研究的目的:
- 为超低能耗和高速神经形态计算引入一类新的量子拓神经晶体管 (QTNs).
- 通过使用QTNs来演示仿生突触行为.
主要方法:
- 使用量子拓绝缘体 (QTI) 材料设计的量子拓神经电阻器 (QTNs).
- 研究了QTI材料中的边缘状态传输和可调节的能量差距,以实现生物启发的神经网络特征.
- 训练有素的QTN使用手势游戏与人工神经网络接口进行决策任务.
主要成果:
- QTN的能量消耗超低 (pJ) 和开关速度高 (μs).
- 通过增强设备和QTI材料设计,通过增强设备和QTI材料设计,证明了神经形态行为的有效学习,重新学习和忘记阶段.
- 通过训练QTN进行决策操作,成功模拟了实时神经形态效率.
结论:
- 对于下一代神经形态计算来说,QTN 具有显著的潜力.
- 开发的QTN可以为智能机器和人形机器的进步做出贡献.
关键词:
人工神经网络的人工神经网络人工突触是一种人造突触.智能系统 智能系统是智能系统.这些神经形态设备是神经形态设备.神经形态感知是一种神经形态感知.突触装置是一种突触装置.拓绝缘体的拓绝缘体是一个更多相关视频
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