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AIfES:一个下一代边缘AI框架
IEEE transactions on pattern analysis and machine intelligence
|January 18, 2024
概括
嵌入式系统框架的人工智能 (AIfES) 通过在小型设备上实现本地智能来增强边缘AI. 在神经网络的执行时间和内存使用方面,AIfES在执行时间和内存使用方面优于传统框架,如TensorFlow Lite for Microcontrollers.
科学领域:
- 嵌入式系统 嵌入式系统
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 边缘人工智能 (AI) 将机器学习 (ML) 集成到嵌入式设备中进行本地处理.
- 传统的边缘AI框架在硬件灵活性和定制加速器集成方面存在局限性.
- 这些局限性阻碍了在资源有限的环境中采用先进的ML创新.
研究的目的:
- 引入用于嵌入式系统的人工智能框架 (AIfES),旨在解决现有的边缘AI框架的局限性.
- 提供AIfES架构及其基础设计原则的详细概述.
- 在嵌入式硬件上对TensorFlow Lite for Microcontrollers (TFLM) 的AIfES性能进行评估.
主要方法:
- 介绍了AIfES的详细架构概述.
- AIfES和TFLM之间的性能比较是在ARM Cortex-M4系统芯片 (SoC) 上进行的.
- 评估使用完全连接的神经网络 (FCNNs) 和卷积神经网络 (CNNs).
主要成果:
- 对于FCNNs,AIfES在执行时间和内存消耗方面表现优于TFLM.
- 与TFLM相比,AIfES在CNN的内存消耗减少了多达54%.
- AIfES成功地在资源有限的设备 (<100 KB RAM) 上启用了CNN的设备上训练.
结论:
- AIfES为边缘AI应用提供了灵活和高效的框架,克服了传统方法的局限性.
- 该框架支持在高度受限制的嵌入式系统上进行先进的ML任务,包括设备上培训.
- AIfES代表了在现实世界的嵌入式应用中部署复杂的人工智能能力的重大进步.
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