Machine Learning for System Diagnosis
A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".
Deadline for manuscript submissions: closed (1 December 2021) | Viewed by 507
Special Issue Editors
Interests: HPC; resilience; fault tolerance; deep learning; blockchain
Special Issue Information
Dear Colleagues,
Machine learning (ML) is making a strong resurgence in tune with the massive generation of unstructured data, which in turn requires massive computational resources. From edge devices to the cloud, there are many applications that rely on the significant efficiency of ML techniques. However, with technology node scaling, such systems become more susceptible to faults, which in turn can impact the NN accuracy. Faults can be caused by different reasons such as hardware defects, voltage fluctuations, and radiation effects, as well as software issues such as stack overflow, and compiler uncertainty, among others. Dealing with such faults and designing resilient and fault-tolerant ML systems in edge devices and/or the cloud requires novel and efficient hardware-level techniques.
The goal of this Special Issue is to present a collection of fault-tolerant ML system designs to support efficient ML systems that are resilient against hardware faults. This Special Issue’s scope ranges from analyzing and understanding the fault tolerance of state-of-the-art ML systems, designing resilient ML systems, and exploiting such mechanisms in real-world environments.
Potential topics include, but are not limited to:
- Experimentally or theoretically analyzing the fault tolerance of ML systems;
- Designing efficient resilient ML systems on hardware accelerators (e.g., ASIC, FPGA) or general-purpose processors;
- Understanding and improving the fault tolerance of ML systems on the cloud and HPC systems;
- Understanding and improving the fault tolerance of ML systems on edge devices;
- Understanding how ML systems can improve fault tolerance techniques;
- Improving the reliability of hardware and architecture targeted for machine learning;
- Hardware techniques for trading off the reliability of machine learning-based systems with energy, power, security, and performance;
- The reliability of machine learning-based applications on heterogeneous systems.
Dr. Leonardo Bautista-Gomez
Dr. Behzad Salami
Guest Editors
Manuscript Submission Information
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Keywords
- Machine learning
- Fault tolerance
- Hardware reliability
- Approximate computing
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