TinyNS:平台意识的神经象征自动微型机器学习
Swapnil Sayan Saha1, Sandeep Singh Sandha2, Mohit Aggarwal3
1University of California - Los Angeles, Los Angeles, CA, USA.
概括
TinyNS是一个新的框架,用于在资源有限的设备上创建可解释的AI系统. 它优化了符号推理和机器学习模型的边缘应用程序,优于传统方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 嵌入式系统 嵌入式系统
背景情况:
- 在边缘设备上部署具有符号推理的可解释AI是由于资源限制而具有挑战性的.
- 现有的方法在严格的硬件限制下努力平衡象征性完整性和机器学习性能.
研究的目的:
- 介绍TinyNS,一个平台意识的神经符号架构搜索框架.
- 为边缘AI应用程序实现符号和神经运算符的联合优化.
- 促进在微控制器上部署的神经符号模型的创建.
主要方法:
- 开发了TinyNS,用于用于神经符号模型的自动微控制器代码生成的框架.
- 使用无梯度,黑盒贝叶斯优化器,在复杂空间中进行高效的搜索.
- 集成的硬件意识优化,以确保现实世界的部署性.
主要成果:
- 在几个案例研究中,TinyNS成功地部署了微控制器级神经符号模型.
- 该框架自动生成五种类型的神经符号模型的代码.
- 优化的神经符号模型与纯神经或符号方法相比,表现出更高的性能.
结论:
- TinyNS有效地结合了象征性推理和机器学习,用于边缘AI.
- 该框架保证在真实硬件上执行,克服部署挑战.
- TinyNS代表了开发强大的和可解释的AI在资源有限的环境中的重大进步.
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