Conference proceeding
Special Session: Reliability Analysis for AI/ML Hardware
2021 IEEE 39th VLSI Test Symposium (VTS), v 2021-, pp 1-10
25 Apr 2021
Abstract
Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many critical applications. Ensuring the reliability and robustness of the underlying AI/ML hardware becomes our paramount importance. In this paper, we explore and evaluate the reliability of different AI/ML hardware. The first section outlines the reliability issues in a commercial systolic array-based ML accelerator in the presence of faults engendering from device-level non-idealities in the DRAM. Next, we quantified the impact of circuit-level faults in the MSB and LSB logic cones of the Multiply and Accumulate (MAC) block of the AI accelerator on the AI/ML accuracy. Finally, we present two key reliability issues- circuit aging and endurance in emerging neuromorphic hardware platforms and present our system-level approach to mitigate them.
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Details
- Title
- Special Session: Reliability Analysis for AI/ML Hardware
- Creators
- Shamik Kundu - The University of Texas at DallasKanad Basu - The University of Texas at DallasMehdi Sadi - Auburn UniversityTwisha Titirsha - Drexel UniversityShihao Song - Drexel UniversityAnup Das - Drexel UniversityUjjwal Guin - Auburn UniversityIEEE
- Publication Details
- 2021 IEEE 39th VLSI Test Symposium (VTS), v 2021-, pp 1-10
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000681694900026
- Scopus ID
- 2-s2.0-85107495719
- Other Identifier
- 991019238705704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Engineering, Electrical & Electronic