Scalable VLSI Architecture for GF(p) Montgomery Modular Inverse Computation

Modular inverse computation is needed in several public key cryptographic applications. In this work, we present two VLSI hardware implementations used in the calculation of Montgomery modular inverse operation. The implementations are based on the same inversion algorithm, however, one is fixed (fully parallel) and the other is scalable. The scalable design is the novel modification performed on the fixed hardware to make it occupy a small area and operate within better or similar speed. Both hardware designs are compared based on their speed and area. The area of the scalable design is on average 42% smaller than the fixed one. The delay of the designs, however, depends on the actual data size and the maximum numbers the hardware can handle. As the actual data size approach the hardware limit the scalable hardware speedup reduces in comparison to the fixed one, but still its delay is practical

Scalable VLSI Architecture for GF(p) Montgomery Modular Inverse Computation

Modular inverse computation is needed in several public key cryptographic applications. In this work, we present two VLSI hardware implementations used in the calculation of Montgomery modular inverse operation. The implementations are based on the same inversion algorithm, however, one is fixed (fully parallel) and the other is scalable. The scalable design is the novel modification performed on the fixed hardware to make it occupy a small area and operate within better or similar speed. Both hardware designs are compared based on their speed and area. The area of the scalable design is on average 42% smaller than the fixed one. The delay of the designs, however, depends on the actual data size and the maximum numbers the hardware can handle. As the actual data size approach the hardware limit the scalable hardware speedup reduces in comparison to the fixed one, but still its delay is practical

Speeding up a scalable modular inversion hardware architecture

The modular inversion is a fundamental process in several cryptographic systems. It can be computed in software or hardware, but hardware computation proven to be faster and more secure. This research focused on improving an old scalable inversion hardware architecture proposed in 2004 for finite field GF(p). The architecture has been made of two parts, a computing unit and a memory unit. The memory unit is to hold all the data bits of computation whereas the computing unit performs all the arithmetic operations in word (digit) by word bases known as scalable method. The main objective of this project was to investigate the cost and benefit of modifying the memory unit to include parallel shifting, which was one of the tasks of the scalable computing unit. The study included remodeling the entire hardware architecture removing the shifter from the scalable computing part embedding it in the memory unit instead. This modification resulted in a speedup to the complete inversion process with an area increase due to the new memory shifting unit. Quantitative measurements of the speed area trade-off have been investigated. The results showed that the extra hardware to be added for this modification compared to the speedup gained, giving the user the complete picture to choose from depending on the application need.the British council in Saudi Arabia, KFUPM, Dr. Tatiana Kalganova at the Electrical & Computer Engineering Department of Brunel University in Uxbridg

High Speed Hardware Architecture to Compute GF(p) Montgomery Inversion with Scalability Features

Modular inversion is a fundamental process in several cryptographic systems. It can be computed in software or hardware, but hardware computation has been proven to be faster and more secure. This research focused on improving an old scalable inversion hardware architecture proposed in 2004 for finite field GF(p). The architecture comprises two parts, a computing unit and a memory unit. The memory unit holds all the data bits of computation whereas the computing unit performs all the arithmetic operations in word (digit) by word bases such that the design is scalable. The main objective of this paper is to show the cost and benefit of modifying the memory unit to include shifting, which was previously one of the tasks of the scalable computing unit. The study included remodeling the entire hardware architecture removing the shifter from the scalable computing part and embedding it in the non-scalable memory unit instead. This modification resulted in a speedup to the complete inversion process with an area increase due to the new memory shifting unit. Several design schemes have been compared giving the user the complete picture to choose from depending on the application need

Scalable VLSI design for fast GF (p) montgomery inverse computation

This paper accelerates a scalable GF(p) Montgomery inversion hardware. The hardware is made of two parts a memory and a computing unit. We modified the original memory unit to include parallel shifting of all bits which was a task handled by the computing unit. The new hardware modeling, simulating, and synthesizing is performed through VHDL for several 160-bits designs showing interesting speedup to the inverse computation.British council in Saudi Arabia, KFUPM, Electrical & Computer Engineering Department of Brunel University in Uxbridg

Efficient Scalable VLSI Architecture for Montgomery Inversion in GF(p)

The multiplicative inversion operation is a fundamental computation in several cryptographic applications. In this work, we propose a scalable VLSI hardware to compute the Montgomery modular inverse in GF(p). We suggest a new correction phase for a previously proposed almost Montgomery inverse algorithm to calculate the inversion in hardware. We also propose an efficient hardware algorithm to compute the inverse by multi-bit shifting method. The intended VLSI hardware is scalable, which means that a fixed-area module can handle operands of any size. The word-size, which the module operates, can be selected based on the area and performance requirements. The upper limit on the operand precision is dictated only by the available memory to store the operands and internal results. The scalable module is in principle capable of performing infinite-precision Montgomery inverse computation of an integer, modulo a prime number. This scalable hardware is compared with a previously proposed fixed (fully parallel) design showing very attractive results

Customisable arithmetic hardware designs

Imperial Users onl

Scalable and Unified Hardware to Compute Montgomery Inverse in GF(p) and GF(2n)

Computing the inverse of a number in finite fields GF(p) or GF(2n) is equally important for cryptographic applications. This paper proposes a novel scalable and unified architecture for a Montgomery inverse hardware that operates in both GF(p) and GF(2n) fields. We adjust and modify a GF(2n) Montgomery inverse algorithm to accommodate multi-bit shifting hardware, making it very similar to a previously proposed GF(p) algorithm. The architecture is intended to be scalable, which allows the hardware to compute the inverse of long precision numbers in a repetitive way. After implementing this unified design it was compared with other designs. The unified hardware was found to be eight times smaller than another reconfigurable design, with comparable performance. Even though the unified design consumes slightly more area and it is slightly slower than the scalable inverter implementations for GF(p) only, it is a practical solution whenever arithmetic in the two finite fields is needed

Scalable and Unified Hardware to Compute Montgomery Inverse in GF(p) and GF(2n)

Computing the inverse of a number in finite fields GF(p) or GF(2n) is equally important for cryptographic applications. This paper proposes a novel scalable and unified architecture for a Montgomery inverse hardware that operates in both GF(p) and GF(2n) fields. We adjust and modify a GF(2n) Montgomery inverse algorithm to accommodate multi-bit shifting hardware, making it very similar to a previously proposed GF(p) algorithm. The architecture is intended to be scalable, which allows the hardware to compute the inverse of long precision numbers in a repetitive way. After implementing this unified design it was compared with other designs. The unified hardware was found to be eight times smaller than another reconfigurable design, with comparable performance. Even though the unified design consumes slightly more area and it is slightly slower than the scalable inverter implementations for GF(p) only, it is a practical solution whenever arithmetic in the two finite fields is needed

New Hardware Algorithms and Designs for Montgomery Modular Inverse Computation in Galois Fields GF(p) and GF(2n)

The computation of the inverse of a number in finite fields, namely Galois Fields GF(p) or GF(2n), is one of the most complex arithmetic operations in cryptographic applications. In this work, we investigate the GF(p) inversion and present several phases in the design of efficient hardware implementations to compute the Montgomery modular inverse. We suggest a new correction phase for a previously proposed almost Montgomery inverse algorithm to calculate the inversion in hardware. It is also presented how to obtain a fast hardware algorithm to compute the inverse by multi-bit shifting method. The proposed designs have the hardware scalability feature, which means that the design can fit on constrained areas and still handle operands of any size. In order to have long-precision calculations, the module works on small precision words. The word-size, on which the module operates, can be selected based on the area and performance requirements. The upper limit on the operand precision is dictated only by the available memory to store the operands and internal results. The scalable module is in principle capable of performing infinite-precision Montgomery inverse computation of an integer, modulo a prime number. We also propose a scalable and unified architecture for a Montgomery inverse hardware that operates in both GF(p) and GF(2n) fields. We adjust and modify a GF(2n) Montgomery inverse algorithm to benefit from multi-bit shifting hardware features making it very similar to the proposed best design of GF(p) inversion hardware. We compare all scalable designs with fully parallel ones based on the same basic inversion algorithm. All scalable designs consumed less area and in general showed better performance than the fully parallel ones, which makes the scalable design a very efficient solution for computing the long precision Montgomery inverse