用于可扩展的神经形态计算的离子浮动门内存阵列的并行编程
Elliot J Fuller1, Scott T Keene2, Armantas Melianas2
1Sandia National Laboratories, Livermore, CA, USA.
概括
研究人员开发了一种用于神经形态计算的新型离子浮动门存储阵列. 这种系统可实现高效的并行重量更新和低电流读取, 进步人工智能硬件.
科学领域:
- 材料科学
- 计算机工程
- 神经科学
背景情况:
- 传统计算面临效率限制,特别是在人工神经网络中.
- 神经形态计算旨在通过并行处理和专用记忆提高效率.
- 实现高效的学习需要选择性,线性重量更新和低读取电流 (<10 nA).
研究的目的:
- 引入一个新的离子浮动门存储阵列,
- 展示人工神经网络权重的选择性和线性编程.
- 在10纳米安培以下的电流下实现突触重量读取.
主要方法:
- 使用聚合物氧化还原晶体管与导电桥内存 (CBM) 集成.
- 通过克服选择性重量更新的CBM桥接值电压实现并行编程.
- 通过用绝缘体稀释导电聚合物来达到低读数电流.
主要成果:
- 证明了反氧晶体管阵列的选择性和线性编程.
- 在10纳米安培以下的电流下实现了突触重量读取.
- 记忆阵列表现出高耐久性 (>10亿读写操作) 和高频率 (>1 MHz).
结论:
- 开发的离子浮动门记忆阵列满足了高效神经形态学习的关键要求.
- 这项技术为人工智能应用提供了超越传统计算效率的途径.
- 系统显示出强大且高速的神经形态硬件的前景.
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