双终端神经形态设备用于尖端神经网络:神经元,突触和阵列集成
Youngmin Kim1, Ji Hyun Baek1, In Hyuk Im1
1Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea.
ACS nano
|December 12, 2024
概括
使用人工神经网络 (ANN) 的神经形态计算解决了大数据挑战. 这篇评论探讨基于memristor的尖端神经网络 (SNN) 进行类似大脑的计算,重点关注神经元,突触和集成.
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
- 计算神经科学是一种神经科学.
背景情况:
- 传统的计算与庞大而复杂的数据集作斗争.
- 神经形态计算和人工神经网络 (ANN) 提供了潜在的解决方案.
- 尖端神经网络 (SNN) 是一个有前途的第三代ANN,用于实时时空处理.
研究的目的:
- 审查基于memristor的尖端神经网络 (SNN) 神经形态硬件的进展.
- 要突出基于memristor的神经元,突触和阵列集成的作用.
- 为开发下一代类似大脑的计算系统提供视角.
主要方法:
- 专注于用于人工神经元和突触的memristor设备.
- 研究这些组件在数组中的集成.
- 将相关的生物见解纳入硬件设计.
主要成果:
- 基于memristor的设备是SNN硬件的重要组成部分.
- 克服物质和整合挑战是仿生行为的关键.
- 在为多重积累 (MAC) 操作创建高密度横条数组方面正在取得进展.
结论:
- 基于memristor的SNN代表了迈向类似大脑计算的重要一步.
- 材料特性和设备集成对于有效的SNN硬件至关重要.
- 需要进一步的研究才能充分发挥这些系统的潜力.
关键词:
人工神经网络 (ANN) 是一个人工神经网络.人工神经元是一种神经元.人工突触是一种人工突触.生物启发的生物灵感交叉杆阵列是一个交叉杆阵列.深度神经网络 (DNN) 是一种深度神经网络.记忆力 记忆力 记忆力神经形态计算是一种神经形态计算.选择器选择器选择器尖端神经网络 (SNN) 是一个神经网络.更多相关视频
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