عنوان انگلیسی مقاله:
Explainability and Dependability Analysis of Learning Automata based AI Hardware
ترجمه فارسی عنوان مقاله:
تحلیل توضیح و قابلیت اطمینان یادگیری سخت افزار هوش مصنوعی مبتنی بر Automata
IEEE - 2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS);2020; ; ;
Rishad Shafik, Adrian Wheeldon and Alex Yakovlev
Explainability remains the holy grail in designing
the next-generation pervasive artificial intelligence (AI) systems.
Current neural network based AI design methods do not
naturally lend themselves to reasoning for a decision making
process from the input data. A primary reason for this is the
overwhelming arithmetic complexity.
Built on the foundations of propositional logic and game
theory, the principles of learning automata are increasingly
gaining momentum for AI hardware design. The lean logic based
processing has been demonstrated with significant advantages
of energy efficiency and performance. The hierarchical logic
underpinning can also potentially provide opportunities for bydesign
explainable and dependable AI hardware. In this paper,
we study explainability and dependability using reachability
analysis in two simulation environments. Firstly, we use a behavioral
SystemC model to analyze the different state transitions.
Secondly, we carry out illustrative fault injection campaigns in
a low-level SystemC environment to study how reachability is
affected in the presence of hardware stuck-at 1 faults. Our
analysis provides the first insights into explainable decision
models and demonstrates dependability advantages of learning
automata driven AI hardware design.
Keywords: Rainfall | Artificial | Computing | Simulation | Architecture