Implementation of Generic and Efficient Architecture of Elliptic Curve Cryptography over Various GF(p) for Higher Data Security

Elliptic Curve Cryptography (ECC) has recognized much more attention over the last few years and has time-honored itself among the renowned public key cryptography schemes. The main feature of ECC is that shorter keys can be used as the best option for implementation of public key cryptography in resource-constrained (memory, power, and speed) devices like the Internet of Things (IoT), wireless sensor based applications, etc. The performance of hardware implementation for ECC is affected by basic design elements such as a coordinate system, modular arithmetic algorithms, implementation target, and underlying finite fields. This paper shows the generic structure of the ECC system implementation which allows the different types of designing parameters like elliptic curve, Galois prime finite field GF(p), and input type. The ECC system is analyzed with performance parameters such as required memory, elapsed time, and process complexity on the MATLAB platform. The simulations are carried out on the 8th generation Intel core i7 processor with the specifications of 8 GB RAM, 3.1 GHz, and 64-bit architecture. This analysis helps to design an efficient and high performance architecture of the ECC system on Application Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA).Elliptic Curve Cryptography (ECC) has recognized much more attention over the last few years and has time-honored itself among the renowned public key cryptography schemes. The main feature of ECC is that shorter keys can be used as the best option for implementation of public key cryptography in resource-constrained (memory, power, and speed) devices like the Internet of Things (IoT), wireless sensor based applications, etc. The performance of hardware implementation for ECC is affected by basic design elements such as a coordinate system, modular arithmetic algorithms, implementation target, and underlying finite fields. This paper shows the generic structure of the ECC system implementation which allows the different types of designing parameters like elliptic curve, Galois prime finite field GF(p), and input type. The ECC system is analyzed with performance parameters such as required memory, elapsed time, and process complexity on the MATLAB platform. The simulations are carried out on the 8th generation Intel core i7 processor with the specifications of 8 GB RAM, 3.1 GHz, and 64-bit architecture. This analysis helps to design an efficient and high performance architecture of the ECC system on Application Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA)

An efficient hardware architecture for a neural network activation function generator

This paper proposes an efficient hardware architecture for a function generator suitable for an artificial neural network (ANN). A spline-based approximation function is designed that provides a good trade-off between accuracy and silicon area, whilst also being inherently scalable and adaptable for numerous activation functions. This has been achieved by using a minimax polynomial and through optimal placement of the approximating polynomials based on the results of a genetic algorithm. The approximation error of the proposed method compares favourably to all related research in this field. Efficient hardware multiplication circuitry is used in the implementation, which reduces the area overhead and increases the throughput

Self-Partial and Dynamic Reconfiguration Implementation for AES using FPGA

This paper addresses efficient hardware/software implementation approaches for the AES (Advanced Encryption Standard) algorithm and describes the design and performance testing algorithm for embedded system. Also, with the spread of reconfigurable hardware such as FPGAs (Field Programmable Gate Array) embedded cryptographic hardware became cost-effective. Nevertheless, it is worthy to note that nowadays, even hardwired cryptographic algorithms are not so safe. From another side, the self-reconfiguring platform is reported that enables an FPGA to dynamically reconfigure itself under the control of an embedded microprocessor. Hardware acceleration significantly increases the performance of embedded systems built on programmable logic. Allowing a FPGA-based MicroBlaze processor to self-select the coprocessors uses can help reduce area requirements and increase a system's versatility. The architecture proposed in this paper is an optimal hardware implementation algorithm and takes dynamic partially reconfigurable of FPGA. This implementation is good solution to preserve confidentiality and accessibility to the information in the numeric communication

Solution of partial differential equations on vector and parallel computers

The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed

Highly parallel sparse Cholesky factorization

Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms

Report from the MPP Working Group to the NASA Associate Administrator for Space Science and Applications

NASA's Office of Space Science and Applications (OSSA) gave a select group of scientists the opportunity to test and implement their computational algorithms on the Massively Parallel Processor (MPP) located at Goddard Space Flight Center, beginning in late 1985. One year later, the Working Group presented its report, which addressed the following: algorithms, programming languages, architecture, programming environments, the way theory relates, and performance measured. The findings point to a number of demonstrated computational techniques for which the MPP architecture is ideally suited. For example, besides executing much faster on the MPP than on conventional computers, systolic VLSI simulation (where distances are short), lattice simulation, neural network simulation, and image problems were found to be easier to program on the MPP's architecture than on a CYBER 205 or even a VAX. The report also makes technical recommendations covering all aspects of MPP use, and recommendations concerning the future of the MPP and machines based on similar architectures, expansion of the Working Group, and study of the role of future parallel processors for space station, EOS, and the Great Observatories era

Wide-bandwidth high-resolution search for extraterrestrial intelligence

Research accomplished in the following areas is discussed: the antenna configuration; HEMT low-noise amplifiers; the downconverter; the Fast Fourier Transform Array; the backend array; and the backend and workstation