相关实验视频
为高可靠性CNN加速器提供优化的耐故障数据处理模块
Sung-Kwang Yoon1,2, Seung-Han Lee1,2, Juhyeong Jo3
1Department of Electrical and Computer Engineering, Inha University, Incheon, South Korea.
PloS one
|February 27, 2026
概括
我们开发了一种新的模块,用于处理系统阵列中的处理元件 (PE),可以减少硬件大小和功耗. 这一创新提高了可靠性,并最大限度地减少了测试开销,以提高卷积神经网络性能.
科学领域:
- 计算机工程 计算机工程
- 硬件架构 硬件架构
- 人工智能的人工智能
背景情况:
- 卷积神经网络 (CNN) 对于图像推断至关重要.
- 缩阵列架构提供高性能,但面临硬件开销和可靠性挑战.
- 这些系统中现有的处理元件 (PE) 在效率和可靠性之间进行了权衡.
研究的目的:
- 为PEs提出一个新的模块,提高效率和可靠性.
- 为了减少缩阵列系统中的功耗和硬件足迹.
- 为了尽量减少测试的开销,并提高故障检测能力.
主要方法:
- 在PE模块中集成本地二进制模式和min-max操作.
- 优化架构的再利用,以实现高效的故障检测.
- 实施故障PE绕过机制,以确保系统的可靠运行.
主要成果:
- 使用南门45nm库,硬件面积减少了29.03%.
- 在现场可编程门阵列上,动态功耗减少了13.72%.
- 在故障注入实验中,错误减少高达33.57%,测试覆盖率超过94%.
结论:
- 拟议的模块显著提高了PE的效率和可靠性.
- 这种方法为系统阵列中的硬件开销和可靠性权衡提供了切实可行的解决方案.
- 该模块增强了CNN硬件加速,降低了功率和面积.
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