Exploiting the Tradeoff between Program Accuracy and Soft-error Resiliency Overhead for Machine Learning Workloads
Exploiting the Tradeoff between Program Accuracy and Soft-error Resiliency Overhead for Machine Learning Workloads
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发表时间：
2017-07
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通讯作者：
Qingchuan Shi;H. Omar;O. Khan
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作者：
Qingchuan Shi;H. Omar;O. Khan
为了保护多核免受软错误扰动，已经开发了具有高覆盖率但高功率和性能开销的弹性方案。新兴的安全关键机器学习应用程序越来越多地部署在这些平台上。此外，这些系统暴露在恶劣的环境中，例如无人机（UAV）和自动驾驶汽车。由于此类应用程序的独特结构和计算行为，人们已经进行了放宽其准确性以获得性能优势的研究。我们观察到，并非所有瞬态错误都会影响程序的正确性，某些错误只会影响程序的准确性，即程序完成时会与无错误结果存在某些可接受的偏差。本文阐述了使用机器学习工作负载实现跨层软错误弹性的想法，其中引入程序准确性作为权衡，以在未来的大规模多核上提供弹性且高效的执行。
To protect multicores from soft-error perturbations, resiliency schemes have been developed with high coverage but high power and performance overheads. Emerging safety-critical machine learning applications are increasingly being deployed on these platforms. Moreover, these systems are exposed to harsh environments, such as unmanned aerial vehicles (UAVs) and self-driving cars. Due to the unique structure and computational behavior of such applications, research has been done on relaxing their accuracy for performance benefits. We observe that not all transient errors affect program correctness, some errors only affect program accuracy, i.e., the program completes with certain acceptable deviations from error free outcome. This paper illustrates the idea of cross-layer soft-error resilience using machine learning workloads, where program accuracy is introduced as a tradeoff to deliver resilient yet efficient execution on futuristic large-scale multicores.