NoC-Based FPGA Acceleration for Monte Carlo Simulations with Applications to SPECT Imaging

Kinsman, P. J.; Nicolici, Nicola

doi:10.1109/tc.2011.250

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…A significant amount of other related work exists, in which different Monte Carlo simulations are accelerated using FPGAs. This includes image reconstruction for Single-Photon Emission Computed Tomography (SPECT) [11], pricing of Asian options [17], simulation of electron dynamics in semiconductors [16] and simulation of biological cells [25].…”

Section: Monte Carlo Simulations On Fpgasmentioning

confidence: 99%

Towards Real Time Radiotherapy Simulation

Voss

Ziegenhein

Vermond

et al. 2020

J Sign Process Syst

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We propose a novel reconfigurable hardware architecture to implement Monte Carlo based simulation of physical dose accumulation for intensity-modulated adaptive radiotherapy. The long term goal of our effort is to provide accurate dose calculation in real-time during patient treatment. This will allow wider adoption of personalised patient therapies which has the potential to significantly reduce dose exposure to the patient as well as shorten treatment and greatly reduce costs. The proposed architecture exploits the inherent parallelism of Monte Carlo simulations to perform domain decomposition and provide high resolution simulation without being limited by on-chip memory capacity. We present our architecture in detail and provide a performance model to estimate execution time, hardware area and bandwidth utilisation. Finally, we evaluate our architecture on a Xilinx VU9P platform as well as the Xilinx Alveo U250 and show that three VU9P based cards or two Alevo U250s are sufficient to meet our real time target of 100 million randomly generated particle histories per second.

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Section: Monte Carlo Simulations On Fpgasmentioning

confidence: 99%

Towards Real Time Radiotherapy Simulation

Voss

Ziegenhein

Vermond

et al. 2020

J Sign Process Syst

View full text Add to dashboard Cite

show abstract

“…A significant amount of other related work exists, in which different Monte Carlo simulations are accelerated using FPGAs. This includes image reconstruction for Single-Photon Emission Computed Tomography (SPECT) [19], pricing of Asian options [20], simulation of electron dynamics in semiconductors [21] and simulation of biological cells [22].…”

Section: B Monte Carlo Simulations On Fpgasmentioning

confidence: 99%

Towards Real Time Radiotherapy Simulation

Voss

Ziegenhein

Vermond

et al. 2019

2019 IEEE 30th International Conference on Application-Specific Systems, Architectures and Processors (ASAP)

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We propose a novel reconfigurable hardware architecture to implement Monte Carlo based simulation of physical dose accumulation for intensity-modulated adaptive radiotherapy. The long term goal of our effort is to provide accurate online dose calculation in real-time during patient treatment. This will allow wider adoption of personalised patient therapies which has the potential to significantly reduce dose exposure to the patient as well as shorten treatment and greatly reduce costs. The proposed architecture exploits the inherent parallelism of Monte Carlo simulations to perform domain decomposition and provide high resolution simulation without being limited by on-chip memory capacity. We present our architecture in detail and provide a performance model to estimate execution time, hardware area and bandwidth utilisation. Finally, we evaluate our architecture on a Xilinx VU9P platform and show that three cards are sufficient to meet our real time target of 100 million randomly generated particle histories per second.

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“…(ii) In the second technology, our QPPUBG platform is based on the Monte Car lo method [16], and uses the VG dynamic generator to produce the interval histogram for each dimension in the decision space, then obtains the probability density function of uncertain data set. Next, our QPPUBG platform presents the multiple integral expressions with a rigorous correctness proof.…”

Section: Realization For Modulementioning

confidence: 99%