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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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硬件辅助低延迟NPU虚拟化方法用于多传感器AI系统.

Jong-Hwan Jean1, Dong-Sun Kim1

  • 1Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)
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概括
此摘要是机器生成的。

这项研究通过虚拟化神经处理单元 (NPU) 来增强AI处理,同时运行多个模型. 这提高了资源利用率,并减少了实时应用程序 (如自动驾驶) 的延迟.

关键词:
硬件调度器是一个硬件调度器.多传感器多传感器神经处理单元的神经处理单元提前检索 提前检索虚拟化是一种虚拟化.

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科学领域:

  • 人工智能的人工智能
  • 计算机工程 计算机工程
  • 嵌入式系统 嵌入式系统

背景情况:

  • 自动驾驶和智能家居中的AI系统需要处理复杂的文本和图像数据.
  • 当前的多传感器系统面临着低资源利用率和内存延迟的挑战.
  • 整合NPU和传感器可以提高速度,但并不总是提高效率.

研究的目的:

  • 减少人工智能系统的处理时间和提高资源利用率.
  • 为了应对基于NPU的系统中低资源利用和内存延迟的挑战.
  • 在资源有限的环境中实现高效的多任务处理和低延迟处理.

主要方法:

  • 虚拟化神经处理单元 (NPU) 以同时处理多个深度学习模型.
  • 实施硬件调度器来管理任务执行和优化资源分配.
  • 使用数据预检查技术来最大限度地减少内存延迟.

主要成果:

  • 硬件调度器在测试模型中减少了超过10%的内存周期.
  • 观察到记忆延迟的显著减少,例如,NCF的30%和DLRM的70%.
  • 尽量减少NPU空时间和内存延迟,特别是在频繁上下文切换的环境中.

结论:

  • 拟议的虚拟化方法有效地提高了NPU资源利用率,并减少了处理延迟.
  • 这种方法对于需要高效的多任务处理的实时人工智能应用非常有益.
  • 在资源有限的环境中实现了优化性能,支持自动驾驶和智能家居等应用程序.