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pro vyhledávání: '"Huang, Haitong"'
Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics. Since deep learning is known to be computing- and m
Externí odkaz:
http://arxiv.org/abs/2312.13754
Autor:
Luo, Erjing, Huang, Haitong, Liu, Cheng, Li, Guoyu, Yang, Bing, Wang, Ying, Li, Huawei, Li, Xiaowei
Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for a deep learning task promise significant advantages of both inference accuracy and computing overhead. FPGAs with fine-grained reconfiguration capability can ad
Externí odkaz:
http://arxiv.org/abs/2308.11334
Autor:
Xue, Xinghua, Liu, Cheng, Liu, Bo, Huang, Haitong, Wang, Ying, Luo, Tao, Zhang, Lei, Li, Huawei, Li, Xiaowei
Winograd is generally utilized to optimize convolution performance and computational efficiency because of the reduced multiplication operations, but the reliability issues brought by winograd are usually overlooked. In this work, we observe the grea
Externí odkaz:
http://arxiv.org/abs/2308.08230
Autor:
Huang, Haitong, Liu, Cheng
The reliability of deep learning accelerators (DLAs) used in autonomous driving systems has significant impact on the system safety. However, the DLA reliability is usually evaluated with low-level metrics like mean square errors of the output which
Externí odkaz:
http://arxiv.org/abs/2306.11759
To ensure resilient neural network processing on even unreliable hardware, comprehensive reliability analysis against various hardware faults is generally required before the deep neural network models are deployed, and efficient error injection tool
Externí odkaz:
http://arxiv.org/abs/2306.11758
Autor:
Xue, Xinghua, Liu, Cheng, Huang, Haitong, Liu, Bo, Wang, Ying, Yang, Bing, Luo, Tao, Zhang, Lei, Li, Huawei, Li, Xiaowei
Vision Transformers (ViTs) with outstanding performance becomes a popular backbone of deep learning models for the main-stream vision tasks including classification, object detection, and segmentation. Other than the performance, reliability is also
Externí odkaz:
http://arxiv.org/abs/2302.10469
Autor:
Huang, Haitong, Xue, Xinghua, Liu, Cheng, Wang, Ying, Luo, Tao, Cheng, Long, Li, Huawei, Li, Xiaowei
Soft errors in large VLSI circuits pose dramatic influence on computing- and memory-intensive neural network (NN) processing. Understanding the influence of soft errors on NNs is critical to protect against soft errors for reliable NN processing. Pri
Externí odkaz:
http://arxiv.org/abs/2210.05876
Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than the computing efficiency, we observe its great potential in improving
Externí odkaz:
http://arxiv.org/abs/2202.08675
Akademický článek
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Publikováno v:
In Journal of Electroanalytical Chemistry 1 August 2021 894
