A scalable and configurable Multiprocessor System-on-Chip (MPSoC) virtual platform for hardware and software co-design and co-verification

Wicaksana, Arya; Tang, C. M.; Ng, M. S.

doi:10.1109/conmedia.2015.7449148

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…This SDR platform has highlighted one of vast possibilities offered by the RUMPS401 as a low-power MPSoC on complex applications and serves as a proof of the first-time success Application Specific Integrated Circuits -Technologies, Digital Systems and Design Methodologies ASIC design methodology embraced by UTAR VLSI Research Center. Another example of RUMPS401 application is AES encryption as presented in [17]. The AES application has been successfully tested on the virtual prototyping platform and the RUMPS401 chip.…”

Section: Software-defined Radiomentioning

confidence: 99%

Case Study: First-Time Success ASIC Design Methodology Applied to a Multi-Processor System-on-Chip

Wicaksana¹,

Halim²,

Hartono³

et al. 2019

Application Specific Integrated Circuits - Technologies, Digital Systems and Design Methodologies

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Achieving first-time success is crucial in the ASIC design league considering the soaring cost, tight time-to-market window, and competitive business environment. One key factor in ensuring first-time success is a well-defined ASIC design methodology. Here we propose a novel ASIC design methodology that has been proven for the RUMPS401 (Rahman University Multi-Processor System 401) Multiprocessor System-on-Chip (MPSoC) project. The MPSoC project is initiated by Universiti Tunku Abdul Rahman (UTAR) VLSI design center. The proposed methodology includes the use of Universal Verification Methodology (UVM). The use of electronic design automation (EDA) software during each step of the design methodology is also presented. The first-time success RUMPS401 demonstrates the use of the proposed ASIC design methodology and the good of using one. Especially this project is carried on in educational environment that is even more limited in budget, resources and know-how, compared to the business and industrial counterparts. Here a novel ASIC design methodology that is tailored to first-time success MPSoC is presented.

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Section: Software-defined Radiomentioning

confidence: 99%

Case Study: First-Time Success ASIC Design Methodology Applied to a Multi-Processor System-on-Chip

Wicaksana¹,

Halim²,

Hartono³

et al. 2019

Application Specific Integrated Circuits - Technologies, Digital Systems and Design Methodologies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, this paper proposes the implementation of Shor's quantum algorithm using the ProjectQ framework. The performance of the implementation is evaluated on two different hardware and measured by the execution time (user time) [16], [17]. The results are compared with implementation done by the Quantum Computing Playground and implementation done by the ProjectQ.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Shor’s Quantum Factoring Algorithm Using ProjectQ Framework

Wicaksono¹,

Wicaksana²

2019

IJEAT

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Prime number factorization is a problem in computer science where the solution to that problem takes super-polynomial time classically. Shor’s quantum factoring algorithm is able to solve the problem in polynomial time by harnessing the power of quantum computing. The implementation of the quantum algorithm itself is not detailed by Shor in his paper. In this paper, an approach and experiment to implement Shor’s quantum factoring algorithm are proposed. The implementation is done using Python and a quantum computer simulator from ProjectQ. The testing and evaluation are completed in two computers with different hardware specifications. User time of the implementation is measured in comparison with other quantum computer simulators: ProjectQ and Quantum Computing Playground. This comparison was done to show the performance of Shor’s algorithm when simulated using different hardware. There is a 33% improvement in the execution time (user time) between the two computers with the accuracy of prime factorization in this implementation is inversely proportional to the number of qubits used. Further improvements upon the program that has been developed for this paper is its accuracy in terms of finding the factors of a number and the number of qubits used, as previously mentioned.

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“…This is done to derive information on how the performance of the implementation would be differ between the two computers, since the execution of the implementation is carried out using a quantum simulator [10]. The performance results are measured by the execution time, specifically the user time [11], [12]. The result is compared with QCP's implementation.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Grover’s Quantum Search Algorithm using Rigetti Forest

Anthony¹,

Wicaksana²

2019

IJEAT

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Grover’s quantum search algorithm allows quadratic speedup in unsorted search problem by utilizing amplitude amplification trick in quantum computing. In this paper, an approach to implement Grover’s quantum search algorithm is proposed. The implementation is done using Rigetti Forest and Python. The testing and evaluation processes are carried on in two computers with different hardware specifications to derive more information from the result. The results are measured in user time and compared with implementation from Quantum Computing Playground. The user time of this implementation for 10 qubits and 1024 data is slower compared to Quantum Computing Playground’s implementation. The proposed implementation can be improved by calculating the probability of Grover’s quantum search algorithm in finding the appropriate search result.

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A scalable and configurable Multiprocessor System-on-Chip (MPSoC) virtual platform for hardware and software co-design and co-verification

Cited by 5 publications

References 3 publications

Case Study: First-Time Success ASIC Design Methodology Applied to a Multi-Processor System-on-Chip

Case Study: First-Time Success ASIC Design Methodology Applied to a Multi-Processor System-on-Chip

Implementation of Shor’s Quantum Factoring Algorithm Using ProjectQ Framework

Implementation of Grover’s Quantum Search Algorithm using Rigetti Forest

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