Dynamic circuit specialisation for key-based encryption algorithms and DNA alignment

Parameterised reconfiguration is a method for dynamic circuit specialization on FPGAs. The main advantage of this new concept is the high resource efficiency. Additionally, there is an automated tool flow, TMAP, that converts a hardware design into a more resource-efficient run-time reconfigurable design without a large design effort. We will start by explaining the core principles behind the dynamic circuit specialization technique. Next, we show the possible gains in encryption applications using an AES encoder. Our AES design shows a 20.6% area gain compared to an unoptimized hardware implementation and a 5.3% gain compared to a manually optimized third-party hardware implementation. We also used TMAP on a Triple-DES and an RC6 implementation, where we achieve a 27.8% and a 72.7% LUT-area gain. In addition, we discuss a run-time reconfigurable DNA aligner. We focus on the optimizations to the dynamic specialization overhead. Our final design is up to 2.80-times more efficient on cheaper FPGAs than the original DNA aligner when at least one DNA sequence is longer than 758 characters. Most sequences in DNA alignment are of the order 2^13

Effective arithmetic in finite fields based on Chudnovsky's multiplication algorithm

International audienceThanks to a new construction of the Chudnovsky and Chudnovsky multiplication algorithm, we design efficient algorithms for both the exponentiation and the multiplication in finite fields. They are tailored to hardware implementation and they allow computations to be parallelized, while maintaining a low number of bilinear multiplications.À partir d'une nouvelle construction de l'algorithme de multiplication de Chudnovsky et Chudnovsky, nous concevons des algorithmes efficaces pour la multiplication et l'exponentiation dans les corps finis. Ils sont adaptés à une implémentation matérielle et sont parallélisables, tout en gardant un nombre de multiplications bilinéaires très bas

Quantifying Resource Use in Computations

It is currently not possible to quantify the resources needed to perform a computation. As a consequence, it is not possible to reliably evaluate the hardware resources needed for the application of algorithms or the running of programs. This is apparent in both computer science, for instance, in cryptanalysis, and in neuroscience, for instance, comparative neuro-anatomy. A System versus Environment game formalism is proposed based on Computability Logic that allows to define a computational work function that describes the theoretical and physical resources needed to perform any purely algorithmic computation. Within this formalism, the cost of a computation is defined as the sum of information storage over the steps of the computation. The size of the computational device, eg, the action table of a Universal Turing Machine, the number of transistors in silicon, or the number and complexity of synapses in a neural net, is explicitly included in the computational cost. The proposed cost function leads in a natural way to known computational trade-offs and can be used to estimate the computational capacity of real silicon hardware and neural nets. The theory is applied to a historical case of 56 bit DES key recovery, as an example of application to cryptanalysis. Furthermore, the relative computational capacities of human brain neurons and the C. elegans nervous system are estimated as an example of application to neural nets.Comment: 26 pages, no figure

KLEIN: A New Family of Lightweight Block Ciphers

Resource-efficient cryptographic primitives become fundamental for realizing both security and efficiency in embedded systems like RFID tags and sensor nodes. Among those primitives, lightweight block cipher plays a major role as a building block for security protocols. In this paper, we describe a new family of lightweight block ciphers named KLEIN, which is designed for resource-constrained devices such as wireless sensors and RFID tags. Compared to the related proposals, KLEIN has advantage in the software performance on legacy sensor platforms, while in the same time its hardware implementation can also be compact

Quantifying Shannon's Work Function for Cryptanalytic Attacks

Attacks on cryptographic systems are limited by the available computational resources. A theoretical understanding of these resource limitations is needed to evaluate the security of cryptographic primitives and procedures. This study uses an Attacker versus Environment game formalism based on computability logic to quantify Shannon's work function and evaluate resource use in cryptanalysis. A simple cost function is defined which allows to quantify a wide range of theoretical and real computational resources. With this approach the use of custom hardware, e.g., FPGA boards, in cryptanalysis can be analyzed. Applied to real cryptanalytic problems, it raises, for instance, the expectation that the computer time needed to break some simple 90 bit strong cryptographic primitives might theoretically be less than two years.Comment: 19 page