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Lookup NU author(s): Dr Rishad Shafik,
Professor Alex Yakovlev
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2020.
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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 by-design 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
Author(s): Shafik R, Wheeldon A, Yakovlev A
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 26th IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS)
Year of Conference: 2020
Online publication date: 05/08/2020
Acceptance date: 13/07/2020
Date deposited: 21/10/2020
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