通过学习到学习的过程,通过基于相变内存的内存计算进行快速学习
Thomas Ortner1, Horst Petschenig2, Athanasios Vasilopoulos1
1IBM Research Europe - Zurich, Rüschlikon, Switzerland.
Nature communications
|February 1, 2025
概括
本研究介绍了使用学习到学习 (L2L) 和内存计算神经形态硬件 (NMHW) 的高效人工智能 (AI) 模型. 这些人工智能系统迅速适应新任务,使用最小的数据和计算,性能与软件模型相比.
科学领域:
- 人工智能的人工智能
- 神经形态计算是一种神经形态计算.
- 硬件加速器 硬件加速器
背景情况:
- 当前的人工智能模型需要大量的资源和数据来适应,限制了边缘应用.
- 人类学习证明了高效的知识转移和快速适应新任务.
- 内存计算神经形态硬件 (NMHW) 通过将内存和计算放在一起来模仿大脑原理.
研究的目的:
- 开发低功耗,自主学习的人工智能系统,能够在边缘快速适应.
- 将学习到学习 (L2L) 原则与内存计算神经形态硬件 (NMHW) 集成.
- 用最少的数据和计算力度来证明高效的AI模型适应.
主要方法:
- 配对L2L与NMHW基于相变存储器件.
- 在NMHW上实施AI模型,以适应现实世界的任务.
- 利用软件中的超级训练来准备高精度模型.
主要成果:
- 在图像分类和机器人手臂控制方面展示了AI模型的多功能性.
- 在NMHW上实现了快速学习,只有很少的参数更新.
- NMHW部署的模型与相应的软件相等地执行.
结论:
- 与NMHW相结合的L2L可以为边缘应用程序提供高效,快速适应的AI.
- 提出的方法减少了人工智能模型适应的计算和数据要求.
- 基于软件的超级培训简化了硬件集成和准确性问题.
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