CREMON: Cryptography Embedded on the Convolutional Neural Network Accelerator

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-12
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, v.67, no.12, pp.3337 - 3341

ISSN
1549-7747
DOI
10.1109/tcsii.2020.2971580
URI
http://hdl.handle.net/10203/279511
Appears in Collection
EE-Journal Papers(저널논문)
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