SHF: Small: Collaborative Research: Resilient Computing Systems Using Deep Learning Techniques

在过去的十年中，计算机系统容易出现各种各样的硬件故障。传统上，硬件故障是通过在低于峰值计算效率的情况下运行系统来规避的，从而有效地牺牲了效率来实现可靠性。这种保守的方法不再是一个可行的选择，因为它会导致显著的能源效率低下。由于包含数千台计算机的数据中心是最大和增长最快的电力消费者之一，因此将硬件故障与能源效率之间的关系解耦非常重要。pi的研究将为智能计算系统奠定基础，该系统可以以最高效率运行，但使用基于机器学习的深度学习技术来管理其故障弹性和可靠性。实际上，每当深度神经网络预测到故障时，系统就会学会避开危险。该研究将解决几个重要问题，涉及深度学习技术的可扩展性、灵活性和效率，以应对各种类型的硬件故障。如果成功，该研究产品将最大限度地减少（如果不能消除）对系统的惩罚，这些惩罚源于各种电路和微架构技术，这些技术通常用于减轻和克服硬件故障。

Over the past decade, computer systems have become prone to a variety of hardware failures. Traditionally, hardware failures were circumvented by operating the system at less than peak computing efficiency, effectively compromising efficiency to achieve reliability. Such a conservative approach is no longer a viable option because it leads to significant energy inefficiency. Since datacenters containing thousands of computers are one of the largest and fastest growing consumers of electricity, it is important to decouple the relationship between hardware failures and energy efficiency. The PIs' research will lay the groundwork for an intelligent computing system that operates at peak efficiency, but manages its fault resiliency and reliability using machine-learning based deep learning techniques. In effect, the system learns to steer itself clear of danger whenever its deep neural nets anticipate a failure. The research will address several important issues involving the scalability, flexibility and efficiency of deep learning techniques for various types of hardware failures. If successful, the research product will minimize, if not eliminate, penalties to the system that stem from the various circuit and micro-architectural techniques that are commonly used to mitigate and overcome hardware failures.