视角:异质,树突计算的光电子未来
Luis El Srouji1, Mahmoud Abdelghany1, Hari Rakul Ambethkar1
1Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, United States.
Frontiers in neuroscience
|May 3, 2024
概括
推进神经网络计算需要新的架构. 本文探讨了使用光子学和CMOS电路进行高速,高带宽计算的光电子,树突神经形态计算.
科学领域:
- 计算机工程 计算机工程
- 神经科学是一个神经科学.
- 材料科学 材料科学 材料科学
背景情况:
- 对大型神经网络模型的日益增长的需求需要新的计算架构.
- 协同集成光子学和CMOS电路的进步提供了新的可能性.
- 高带宽光学网络和高速计算对于未来的应用至关重要.
研究的目的:
- 讨论神经形态计算架构当前的趋势.
- 概述神经形态计算的未来方向.
- 探索光电子,异质,树突架构的潜力.
主要方法:
- 审查当前的神经形态计算趋势.
- 光子和CMOS集成的分析.
- 光电子树突架构的概念化.
主要成果:
- 确定塑造神经形态计算的关键趋势.
- 对增强计算的光电子方法的建议.
- 强调光子学和CMOS之间的协同作用.
结论:
- 异质,树突的神经形态计算提供了一个有前途的前进道路.
- 光电子集成是实现AI高性能计算的关键.
- 未来的架构将利用光学和电子元件来提高效率.
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