In the design of a deep neural network (DNN) inference engine, typically, we use some computation reduction techniques to boost the energy efficiency and performance of the engine. Although the DNN has the fault resilience capability in nature, those techniques result in the degradation of inference accuracy. How to analyze the robustness, i.e., the influence of inference accuracy caused by the computation reduction techniques, is an important issue. On the other hand, the DNN inference engine might have soft errors or faults during the life time, which results in the degradation of inference accuracy as well. How to take advantage of the self-resilience capability of DNN and hardware redundancy to enhance the reliability and robustness of DNN engines is also an important issue. In this project, therefore, we will develop the analysis platform of robustness and reliability of DNN inference engine and hardware fault tolerance techniques.
|Effective start/end date||1/08/21 → 31/07/22|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Deep neural network
- fault tolerance
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