一个具有芯片内学习的神经形态处理器,用于超越CMOS的设备集成
Hugh Greatorex1,2, Ole Richter3,4, Michele Mastella5
1Bio-Inspired Circuits and Systems (BICS) Lab, Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands. h.r.greatorex@rug.nl.
Nature communications
|July 11, 2025
概括
本研究介绍了一种混合信号的神经形态架构,用于整合新兴的记忆设备和芯片上学习. 它弥合了基于的系统和用于大脑启发的计算的先进材料之间的差距.
科学领域:
- 神经形态工程的神经形态工程
- 材料科学 材料科学 材料科学
- 计算机架构 计算机架构
背景情况:
- 新兴的记忆技术为神经形态系统提供了潜力,但面临着整合的挑战.
- 在材料开发和大规模的功能神经形态系统实现之间存在差距.
- 为特定功能和CMOS集成选择最佳设备和材料至关重要.
研究的目的:
- 展示一种混合信号的神经形态架构,用于探索芯片上学习和新型设备集成.
- 作为一个平台,将基于的神经形态计算和新兴设备连接起来.
- 通过测试和模拟来证明架构对设备集成的准备.
主要方法:
- 混合信号神经形态架构的开发.
- 整合芯片上学习电路和新型两端和三端设备.
- 进行全面的测量和模拟,以验证设备集成的准备.
主要成果:
- 证明了架构能够集成新兴设备的能力.
- 验证了平台适合测试生物启发的学习算法.
- 建立了大脑启发的计算和设备研究之间的切实联系.
结论:
- 呈现的架构已经准备好将新型记忆设备集成到神经形态系统中.
- 该平台以生物灵感算法促进新兴设备的实际测试.
- 这项工作弥合了先进材料和功能神经形态电子系统之间的差距.
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