Reliability and Safety of Artificial Intelligence Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 May 2024 | Viewed by 133
Special Issue Editors
Interests: AI safety; fault injections; testing; DNN reliability
Interests: fault-tolerant architectures; fault injections; DNN reliability; approximate computing
Interests: microprocessor reliability; ANN reliability; CAD; bioinspired heuristics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The complexity of modern systems has increased over the past few decades. Together with the shrinking of technology nodes, even more systems leverage artificial intelligence (AI) to cope with their ever-growing computing requirements. Some AI models like deep neural networks (DNNs) are excelling in applications like object recognition, autonomous driving, and more applications that require designing custom AI hardware accelerators cut for embedded solutions, particularly for edge devices.
Even though this is a promising solution, it could be dangerous from the perspectives of reliability and safety. Although DNNs exhibit some inherent fault tolerance, recent research reveals their susceptibility to random hardware faults, affecting application performance. Ensuring HW-DNN reliability is essential, especially for safety-critical uses.
This Special Issue is devoted to topics including but not limited to:
- Design and dependability for AI;
- Fault injection-based approaches for DNN reliability;
- On-line testing for AI systems;
- Hardening techniques for AI systems;
- Fault-tolerant/cost tradeoff for embedded AI systems;
- Fault-tolerant AI-oriented HW architectures;
- Approximate computing for AI systems.
Dr. Annachiara Ruospo
Dr. Deveautour Bastien
Prof. Dr. Ernesto Sanchez
Guest Editors
Manuscript Submission Information
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Keywords
- AI safety and reliability
- fault-tolerant architectures
- edge computing
- approximate computing
- hardening techniques
