Hardware Implementation of Efficient Elliptic Curve Scalar Multiplication using Vedic Multiplier

Kadu, Rakesh; Adane, Dattatraya

doi:10.17762/ijcnis.v11i2.3763

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…FPGAs nowadays are widely used to implement vast number of applications. Examples of such applications are presen t ed in [31] and [32]. FPGAs contain a matrix of configurable logic blocks (CLBs).…”

Section: Experimental Results and Comparisonsmentioning

confidence: 99%

FPGA Implementation of Data Flow Graphs for Digital Signal Processing Applications

AL-Zu'bi

Al-Khaleel

Shatnawi

2022

Int. j. commun. netw. inf. secur.

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A rapid growth in digital signal processing applications has increased the requirement for high-speed digital systems. Multiprocessor systems are the best choice for these applications. A prior sequence of operations should be applied to the operations that described the nature of these applications before hardware implementation is produced. These operations should be scheduled and hardware allocated. This paper proposes a new scheduling technique for digital signal processing (DSP) applications has been represented by data flow graphs (DFGs). In addition, hardware allocation is implemented in the form of embedded system. A proposed scheduling technique also achieves the optimal scheduling of a DFG at design time. The optimality criteria considered in this algorithm are the maximum throughput within the available hardware resources. The maximum throughput is achieved by arranging the DFG nodes according to their inter-related data dependencies. Then, two nodes can be clustered into one compound task to reduce the overall execution time by minimizing the number of tasks to be executed that minimizing the number of cycles to execute them. Then each task is presented in form of instruction to be executed in the hardware system. A hardware system is composed of one or multiple homogenous pipelined processing elements and it is designed to meet the maximum-rate schedule. Two implementations are proposed of the system architecture according to the number of the processing elements, namely: the serial system and the parallel system. The serial system comprises one processing element where all tasks are processed sequentially, whilst the parallel system has four processing elements to execute tasks concurrently. These systems consist mainly of seven units: central shared memory, state table, multiway function unit buffer, execution array, processing element/s, instruction buffer and the address generation unit. The hardware components were built on an FPGA chip using Verilog HDL. In synthesis results, the parallel system has better system performance by 25.5% than the serial system. While the serial system requires smaller area size, which described by the number of slice registers and the number of the slice lookup tables (LUTs) than the parallel one. The relationship between the number of instructions that are executed in both systems, and the system area and the system performance that presented by system frequency, are studied. By increasing memories size in both systems, the system performance isn’t affected as in a serial system, and it is slightly decreased as the parallel system by 1.5% to 4.5%. In terms of the systems area, both serial system area and parallel system area are increased and in some cases are doubled. The proposed scheduling technique is shown to outperform the retaining technique, which we have chosen to compare with. The serial system has better performance by 19.3% higher system frequency than a retiming technique. And the parallel system also outperforms the retaining technique by 51.2% higher system frequency in synthesis results.

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Section: Experimental Results and Comparisonsmentioning

confidence: 99%