HyCA：一种面向容错深度学习的混合计算架构
HyCA: A Hybrid Computing Architecture for Fault Tolerant Deep Learning
摘要：
典型的深度学习加速器（DLA）的常规二维计算阵列上的硬件故障可能导致预测准确性大幅下降。现有的冗余设计方法通常具有每个同构冗余处理元件（PE），以减轻2-D计算阵列的有限区域而不是整个计算阵列的故障PE，以避免过多的硬件开销。然而，当任何区域中的故障PE的数量超过同一区域中的冗余PE的数量时，它们无法恢复计算阵列。当故障注入率增加且故障分布不均匀时，失配问题恶化。为了解决这个问题，我们提出了一个混合计算架构（HyCA）的容错的DLAs。它有一组点产生处理单元（DPPU），用于重新计算映射到故障PE的所有操作，而不管故障PE的位置。根据我们的实验，HyCA显示出显着更高的可靠性，可扩展性和性能，更少的芯片面积的惩罚相比，传统的冗余方法。此外，通过利用灵活的重新计算，HyCA也可以用来扫描整个二维计算阵列，并在运行时有效地检测故障PE。
英文摘要：
Hardware faults on the regular 2-D computing array of a typical deep learning accelerator (DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches typically have each homogeneous redundant processing element (PE) to mitigate faulty PEs for a limited region of the 2-D computing array rather than the entire computing array to avoid the excessive hardware overhead. However, they fail to recover the computing array when the number of faulty PEs in any region exceeds the number of redundant PEs in the same region. The mismatch problem deteriorates when the fault injection rate rises and the faults are unevenly distributed. To address the problem, we propose a hybrid computing architecture (HyCA) for fault-tolerant DLAs. It has a set of dot-production processing units (DPPUs) to recompute all the operations that are mapped to the faulty PEs despite the faulty PE locations. According to our experiments, HyCA shows significantly higher reliability, scalability, and performance with less chip area penalty when compared to the conventional redundancy approaches. Moreover, by taking advantage of the flexible recomputing, HyCA can also be utilized to scan the entire 2-D computing array and detect the faulty PEs effectively at runtime.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
