Testability and Dependability of AI Hardware: Survey, Trends, Challenges, and Perspectives - Circuits Intégrés Numériques et Analogiques Access content directly
Journal Articles IEEE Design & Test Year : 2023

Testability and Dependability of AI Hardware: Survey, Trends, Challenges, and Perspectives

Fei Su
  • Function : Author
  • PersonId : 1220958
Chunsheng Liu
  • Function : Author
  • PersonId : 1220959

Abstract

In recent years, there has been an expedited trend in embracing bold and radical innovation of computer architectures, aiming at the continuation of computing performance improvement despite the slowed-down physical device scaling. One new frontier in this field focuses on Artificial Intelligence (AI) hardware. While functionality of AI hardware still remains the main focus, testability and dependability of these new architectures need to be addressed before the mainstream adoption. This survey paper covers the state-of-the-art in research and development of dependability and testability solutions for AI hardware including digital or analog implementations of Artificial Neural Networks (ANNs) and Spiking Neural Networks (SNNs), used in accelerators and neuromorphic designs. Trends, challenges and perspectives are also discussed in this paper.
Fichier principal
Vignette du fichier
Testability_and_Dependability_of_AI_Hardware__Survey__Trends__Challenges__and_Perspectives.pdf (7.55 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03961502 , version 1 (29-01-2023)

Identifiers

Cite

Fei Su, Chunsheng Liu, Haralampos-G. Stratigopoulos. Testability and Dependability of AI Hardware: Survey, Trends, Challenges, and Perspectives. IEEE Design & Test, 2023, 40 (2), pp.8 - 58. ⟨10.1109/MDAT.2023.3241116⟩. ⟨hal-03961502⟩
62 View
176 Download

Altmetric

Share

Gmail Facebook X LinkedIn More