Abstract
Artificial intelligence (AI) hardware, including AI accelerators and neuromorphic computing processors, emerges as one new frontier in the field of computing. There is an expedited paradigm shift in embracing bold and radical innovation of computer architectures, aiming at the continuation of computing performance improvement despite the slowed-down physical device scaling. Testability and dependability of AI hardware need to be addressed before mainstream adoption, especially in latency or throughput-critical, safety-critical, mission-critical, or remotely controlled applications (e.g., computer vision, autonomous driving, smart healthcare, IoTs, and robotics).
Cite
CITATION STYLE
Su, F., Liu, C., & Stratigopoulos, H. G. (2023, April 1). Special Issue on Testability and Dependability of Artificial Intelligence Hardware. IEEE Design and Test. IEEE Computer Society. https://doi.org/10.1109/MDAT.2023.3241114
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