CREMON: Cryptography Embedded on the Convolutional Neural Network Accelerator

Yeongjae Choi, Jaehyeong Sim, Lee Sup Kim

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Due to their excellent performance, tremendous progress has been made in the development of convolutional neural network (CNN) algorithms and efficient CNN accelerators for edge devices. At the same time, security concerns about CNN processing have increased regarding user privacy and safety. In this brief, we focus on developing an efficient data ciphering system embedded in a CNN accelerator. The number of operations of CNN and security workloads, AES-128 in our system, constantly changes during execution, thereby varying their relative ratio. To efficiently support various convolution/AES workloads, we propose CREMON, a reconfigurable system with a cryptography reconfigurable processing element (CRPE). A test chip with the proposed scheme was implemented and tested for performance verification. As a result, the CREMON prototype chip achieved state-of-the-art performance/area efficiency for AES and improved energy efficiency by up to 44.1% in processing CNN/AES workloads.

Original languageEnglish
Article number8981800
Pages (from-to)3337-3341
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Issue number12
StatePublished - Dec 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea grant funded by the Korea Government (MSIP) under Grant NRF-2017R1A2B2009380, and in part by the Samsung Advanced Institute of Technology.

Publisher Copyright:
© 2004-2012 IEEE.


  • AES hardware
  • CNN accelerator
  • Security in CNN processing
  • energy-efficient hardware
  • reconfigurable processor


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