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A Method for Growing Bio-memristors from Slime Mold
Published on: November 2, 2017
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在线和离线学习神经形态应用程序的1kB交叉条数组上利用自满多层实现的记忆架构
Sungjoon Kim1, Hyeonseung Ji2, Kyungchul Park3
1Department of AI Semiconductor Engineering, Korea University, Sejong 30019, Republic of Korea.
ACS nano
|August 21, 2024
概括
在memristor交叉杆阵列中的自约性 (SC) 能够在神经形态系统中实现无晶体管运行和向量矩阵乘法. 这项技术在MNIST分类任务中实现了高精度,证明了它对先进的人工智能硬件的潜力.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 人工智能的人工智能
背景情况:
- 记忆器交叉阵列为神经形态计算提供了一个有前途的硬件平台.
- 挑战包括控制导电丝 (CF) 的形成,并实现高效的计算.
研究的目的:
- 调查自合规 (SC) 在高密度横杆阵列中的实际含义.
- 为了证明使用SC记忆元的无晶体管操作和矢量矩阵乘法 (VMM) 的可行性.
- 在神经网络应用中评估SC memristor交叉条数组的性能.
主要方法:
- 使用了AlO/TiO内部超越限制结构来实现SC在电阻随机存储器 (RRAM) 中.
- 优化了AlO/TiO结构以减少超标和操作电流,确保均的双极电阻开关.
- 通过广泛的电脉冲刺激来评估突触可塑性 (LTP/LTD).
- 在MNIST分类上实现并测试了一个32 × 32横杆阵列,用于基于尖端神经网络 (SNN) 的VMM.
主要成果:
- SC 启用了跨条阵列的无晶体管运行.
- 优化的AlO/TiO结构表现出均的双极电阻切换和模拟特性.
- 在线学习神经网络使用LTP/LTD特征实现了92.36%的MNIST精度.
- 离线学习神经网络在SC模式下达到了95.87%的准确性.
- 32 × 32 数组展示了基于SNN的VMM用于MNIST分类,与软件相比,准确度仅下降了1.2%.
结论:
- 自我合规是没有晶体管的memristor交叉条阵列的可行策略.
- 开发的AlO/TiO记忆器技术支持神经形态系统的高效VMM.
- 经过证明的MNIST分类准确度凸显了这种硬件在AI加速方面的潜力.
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