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<p>Integrating AI into real-world systems such as autonomous vehicles or interactive assistants requires the use of compute accelerators. Traditional processors such as x86 or ARM CPUs are insufficient. Unfortunately, real-world systems have responsiveness requirements, and research is underdeveloped on guaranteeing such responsiveness for accelerator-using systems. One constraint has been uncertainty about what sort of accelerator is best for such systems. In this paper, we argue that researchers should focus on the GPU as the accelerator of choice for embedded real-time AI workloads. We argue that GPUs are already being widely adopted, provide leading compute density, and are architecturally well-suited for real-world, real-time systems.</p>

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The advantage of the GPU as a real-time AI accelerator

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Joshua Bakita,
  • James H. Anderson

摘要

Integrating AI into real-world systems such as autonomous vehicles or interactive assistants requires the use of compute accelerators. Traditional processors such as x86 or ARM CPUs are insufficient. Unfortunately, real-world systems have responsiveness requirements, and research is underdeveloped on guaranteeing such responsiveness for accelerator-using systems. One constraint has been uncertainty about what sort of accelerator is best for such systems. In this paper, we argue that researchers should focus on the GPU as the accelerator of choice for embedded real-time AI workloads. We argue that GPUs are already being widely adopted, provide leading compute density, and are architecturally well-suited for real-world, real-time systems.