Cellular Automata-Based Parallel Random Number Generators Using FPGAs

Abstract

Cellular computing represents a new paradigm for implementing high-speed massively parallel machines. Cellular automata (CA), which consist of an array of locally connected processing elements, are a basic form of a cellular-based architecture. The use of field programmable gate arrays (FPGAs) for implementing CA accelerators has shown promising results. This paper investigates the design of CA-based pseudo-random number generators (PRNGs) using an FPGA platform. To improve the quality of the random numbers that are generated, the basic CA structure is enhanced in two ways. First, the addition of a superrule to each CA cell is considered. The resulting self-programmable CA (SPCA) uses the superrule to determine when to make a dynamic rule change in each CA cell. The superrule takes its inputs from neighboring cells and can be considered itself a second CA working in parallel with the main CA. When implemented on an FPGA, the use of lookup tables in each logic cell removes any restrictions on how the super-rules should be defined. Second, a hybrid configuration is formed by combining a CA with a linear feedback shift register (LFSR). This is advantageous for FPGA designs due to the compactness of the LFSR implementations. A standard software package for statistically evaluating the quality of random number sequences known as Diehardis used to validate the results. Both the SPCA and the hybrid CA/LFSR were found to pass all the Diehardtests.

Intensive random carrier pulse width modulation for induction motor drives based on hopping between discrete carrier frequencies

“…The RTL level verification and implementation are done using the synthesise tool Xilinx ISE 13.2. Then the designed architecture is configured to the Spartan-6 FPGA (XC6SLX45) device [25][26][27]. The functionality of each block in the architecture is simulated thoroughly using the Modelsim software.…”

Designing Stealthy Trojans with Sequential Logic

“…As a result, some additional nonlinearity must be combined with the state of an LFSR to make a reasonably secure stream cipher. One approach is to combine the output of the LFSR with a cellular automata, as illustrated in Figure 2, to form a good pseudo-random number generator (PRNG) [9]. Cellular automata (CA) are similar in structure to LFSRs except that the registers in CA have a logical relationship with their nearest neighbors in both directions.…”

“…The PRNG's consists of an LFSR and a CA unit. The output of the PRNG is the additive (XOR) combination of select LFSR bits and CA bits as discussed in [9]. The PRNG 1 block consists of a 37 bit long LFSR and a 16 bit CA and outputs 8 bits to encode the data received; PRNG 2 is composed of a 31 bit LFSR and an 8 bit CA.…”

Design of a New Neuro-generator with Neuronal Module to Produce Pseudorandom and Perfectly Pseudorandom Sequences

“…The RNG Random Number Generator is a random number generator that uses sources such as noise from electronic circuits, the quantum effect of semiconductors, the combination of temperature and time, among others [1,6,7]. The TRNG True Random Number Generator is widely used in cryptographic systems and is capable of generating truly random sequences [8,9] and the PRNG uses an RNG as a seed, i.e., initial data [1,5]. Furthermore, it produces pseudorandom or perfectly pseudorandom sequences [5].…”