Can I read Fault-Tolerant Deep Learning Processors on EtoBox?

Fault-Tolerant Deep Learning Processors by Xiaowei Li; Guihai Yan; Cheng Liu is a book available to read on EtoBox.

What is Fault-Tolerant Deep Learning Processors about?

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. HyCA shows significantly higher reliability, scalability, and performance with less chip area penalty when compared to the conventional redundancy approaches. To further optimize the reliability of DLA, we focus on improve the reliability of Resistive Random Access Memory (ReRAM), which h

Author

Xiaowei Li; Guihai Yan; Cheng Liu

Publisher

SPRINGER VERLAG, SINGAPOR

Published

2023

Language

EN

ISBN

9789811985515

Subjects

Engineering, Stem