人工智能的 dendrocentric学习
Kwabena Boahen1,2,3,4,5,6
1Department of Bioengineering, Stanford University, Stanford, CA, USA. boahen@stanford.edu.
Nature
|November 30, 2022
概括
人工智能 (AI) 由于计算需求的增加而面临硬件限制. 这项研究提出了树突中心学习,模仿神经元树突,用于智能手机上的节能AI.
科学领域:
- 计算神经科学
- 人工智能硬件
- 半导体行业的发展趋势
背景情况:
- 人工智能 (AI) 的快速发展需要计算能力的指数增长,特别是浮点乘法.
- 目前半导体行业在芯片乘数密度上的进步 (每两年翻一番) 无法与人工智能的计算加速相匹配 (每两个月翻一番).
- 在3D架构中,密集的乘法器的物理限制,包括信号传输距离和热散射,阻碍了进一步的性能增长.
研究的目的:
- 为人工智能 (AI) 提出一种超越当前硬件的热和物理限制的新方法.
- 在AI中引入树突中心学习作为传统突触学习的替代方案.
- 在移动设备上展示节能AI计算的可行性.
主要方法:
- 一个模拟基于树的学习过程的计算模型的开发.
- 设计以模仿拟的树模型的铁电装置的概念化.
- 拟议的树中心学习人工智能 (合成智能) 模型的能源效率评估.
主要成果:
- 树突中心式学习为超越3D芯片架构的热制约提供了一个潜在的解决方案.
- 这种方法从传统的突触权重转向沿着树突的有序输入,称为树突中心学习.
- 拟议的合成智能模型显示了显著降低功耗的潜力,运行在瓦特级别.
结论:
- 由生物神经元结构所启发的 dendrocentric 学习为人工智能硬件提供了一个范式转变.
- 这种方法可以实现高能效的人工智能计算,有可能在智能手机上运行,而不是需要高功率的云基础设施.
- 拟议的人工智能为克服目前半导体的局限性和实现可持续的人工智能进步提供了途径.
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