1. Fault-Tolerance Mechanism Analysis on NVDLA-Based Design Using Open Neural Network Compiler and Quantization Calibrator
- Author
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Kai-Chiang Wu, Der-Yu Tsai, Ning-Chi Huang, Luba Tang, Shu-Ming Liu, and Ming-Xue Yang
- Subjects
Artificial neural network ,Computer science ,business.industry ,Deep learning ,Quantization (signal processing) ,Fault tolerance ,02 engineering and technology ,computer.software_genre ,Chip ,020202 computer hardware & architecture ,Computer engineering ,Robustness (computer science) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Static random-access memory ,Compiler ,Artificial intelligence ,business ,computer - Abstract
The NVIDIA Deep Learning Accelerator (NVDLA) provides free intellectual property licensing to IC chip vendors and researchers to build a chip that uses deep neural networks for inference applications. The Open Neural Network Compiler (ONNC) provides an extensible compiler, a quantization calibrator and optimization supports for running DNN models on NVDLA-based SoCs. Even with open-sourced NVDLA and ONNC, conducting the development of an AI chip still brings up many productivity issues in the mass production stage, such as SRAM MBIST (Memory Built-In Self Test) fail, scan-chain fail etc. When applying Fault-Tolerance Mechanism in error-tolerant applications such as image classification by using the AI CNN model, this paper presents a light-weight Fault-Tolerance Mechanism to effectively enhance the robustness of NVDLA-based edge AI chip when encountering internal SRAM stuck fault. Our non-accurate MAC calculation for the whole convolution computation leads to a very promising quality of results compared to the case when an exactly accurate convolution operation is used. The Fault-Tolerance Mechanism analysis and design described in this paper can also apply to the similar fixed-point deep learning accelerator design, and opens new opportunities for research as well as product development.
- Published
- 2020
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