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CREMON: Cryptography Embedded on the Convolutional Neural Network Accelerator
- Title
- CREMON: Cryptography Embedded on the Convolutional Neural Network Accelerator
- Authors
- Choi, Yeongjae; Sim, Jaehyeong; Kim, Lee-Sup
- Ewha Authors
- 심재형
- SCOPUS Author ID
- 심재형
- Issue Date
- 2020
- Journal Title
- IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
- ISSN
- 1549-7747
1558-3791
- Citation
- IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS vol. 67, no. 12, pp. 3337 - 3341
- Keywords
- Convolution; Kernel; Hardware; Engines; Cryptography; Throughput; Security in CNN processing; CNN accelerator; AES hardware; reconfigurable processor; energy-efficient hardware
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- 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.
- DOI
- 10.1109/TCSII.2020.2971580
- Appears in Collections:
- 인공지능대학 > 컴퓨터공학과 > Journal papers
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