Bit-sliding: A generic technique for bit-serial implementations of SPN-based primitives: Applications to AES, PRESENT and SKINNY

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Abstract

Area minimization is one of the main efficiency criterion for lightweight encryption primitives. While reducing the implementation data path is a natural strategy for achieving this goal, Substitution-Permutation Network (SPN) ciphers are usually hard to implement in a bit-serial way (1-bit data path). More generally, this is hard for any data path smaller than its Sbox size, since many scan flip-flops would be required for storage, which are more area-expensive than regular flip-flops. In this article, we propose the first strategy to obtain extremely small bit-serial ASIC implementations of SPN primitives. Our technique, which we call bit-sliding, is generic and offers many new interesting implementation trade-offs. It manages to minimize the area by reducing the data path to a single bit, while avoiding the use of many scan flip-flops. Following this general architecture, we could obtain the first bit-serial and the smallest implementation of AES-128 to date (1560 GE for encryption only, and 1738 GE for encryption and decryption with IBM 130 nm standard-cell library), greatly improving over the smallest known implementations (about 30% decrease), making AES-128 competitive to many ciphers specifically designed for lightweight cryptography. To exhibit the generality of our strategy, we also applied it to the PRESENT and SKINNY block ciphers, again offering the smallest implementations of these ciphers thus far, reaching an area as low as 1065 GE for a 64-bit block 128-bit key cipher. It is also to be noted that our bit-sliding seems to obtain very good power consumption figures, which makes this implementation strategy a good candidate for passive RFID tags.

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APA

Jean, J., Moradi, A., Peyrin, T., & Sasdrich, P. (2017). Bit-sliding: A generic technique for bit-serial implementations of SPN-based primitives: Applications to AES, PRESENT and SKINNY. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10529 LNCS, pp. 687–707). Springer Verlag. https://doi.org/10.1007/978-3-319-66787-4_33

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