Module Against Power Consumption Attacks for Trustworthiness of Vehicular AI Chips in Wide Temperature Range

Zongwei ZHU, Jiawei GENG, Mingliang ZHOU*, Bin FANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)


Power consumption attacks monitoring on artificial intelligence (AI) chips play a critical role in the vehicular AI systems. However, most of the current monitoring and management methods focus on the trustworthiness of industrial equipment instead of resource-constrained edge devices. To address the above problem, a closed-loop module for monitoring and management of vehicular AI chips based on fitting and filtering to resist power consumption attacks is proposed in this paper. First, considering the characteristics of power, we propose a raw data correction approach for power monitoring to monitor abnormal power consumption. Second, we address the challenging problem of precision temperature monitoring to monitor the abnormal temperature of the chip, especially in a wide temperature range. Finally, the established method is applied to attack surveillance and transformed into a power consumption management problem solved by dynamic voltage and frequency scaling (DVFS) technology. As the experimental results reveal, compared with existing methods of power and temperature monitoring and power consumption control in wide temperature, our method can achieve significantly improved monitoring and managing performance.

Original languageEnglish
Article number2250012
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number3
Early online date21 Feb 2022
Publication statusPublished - 15 Mar 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 World Scientific Publishing Company.


  • attack detection
  • Chip security
  • power management
  • temperature monitoring
  • vehicular chips
  • wide-range temperature


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