神经内存检查和:用于安全内存推理的错误检测和纠正技术
Luca Parrini1, Taha Soliman1, Benjamin Hettwer1
1Bosch Corporate Research, Renningen, Germany.
概括
人工智能加速器的内存计算面临由于硬件变化的准确性问题. 本研究介绍了检查和代码和共同设计技术,以减轻错误,提高AI准确性,最小的开销.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 记忆技术 记忆技术
背景情况:
- 内存计算为人工智能加速器提供了较低的延迟和功率.
- 新兴记忆技术中的硬件变化可能会降低神经网络的准确性.
- 在安全关键的人工智能应用中,妥协的准确性存在风险.
研究的目的:
- 调查内存计算中的硬件变化的技术相关来源.
- 提出一个架构缓解策略,使用检查和代码来检测和纠正错误.
- 通过硬件-软件联合设计技术,优化解决方案的开销.
主要方法:
- 对硬件变化的技术相关来源的分析.
- 实施一个运行时错误检测和纠正策略,使用两个检查和值代码.
- 应用精度意识的硬件和软件共同设计以实现优化.
主要成果:
- 拟议的解决方案有效地减轻了各种AI算法和技术的精度退化.
- 实现了>95%的原始精度恢复,<40%的面积和<30%的延迟开销.
- 性能优于最先进的解决方案和传统的冗余技术,如三重模块冗余.
结论:
- 开发的缓解策略提高了人工智能加速器内存计算的可靠性.
- 硬件和软件的共同设计对于有效的AI系统的错误缓解至关重要.
- 该方法为安全关键应用中安全和准确的AI提供了一个有希望的解决方案.
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