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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., Computer Engineering

In an effort to make DNA sequencing more accessible and affordable, Oxford Nanopore Technologies developed the MinION: A portable cellphone sized DNA sequencing device. Translating the information from this device to a nucleotide sequence is called basecalling, and is done with the aid of artificial neural networks. In this thesis, we accelerate a neural network using the concept of Dataflow programming. The result is a complete basecalling application that relies on an FPGA based platform to run the compute intensive parts. We achieve up to 1.51x speedup over a high-end server with two Intel Xeon Processors, with a rate of 57,040 bases per second. In addition, our implementation uses up to 90.27\% less energy compared to the original implementation., Computer Engineering

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., Computer Engineering

In an effort to make DNA sequencing more accessible and affordable, Oxford Nanopore Technologies developed the MinION: A portable cellphone sized DNA sequencing device. Translating the information from this device to a nucleotide sequence is called basecalling, and is done with the aid of artificial neural networks. In this thesis, we accelerate a neural network using the concept of Dataflow programming. The result is a complete basecalling application that relies on an FPGA based platform to run the compute intensive parts. We achieve up to 1.51x speedup over a high-end server with two Intel Xeon Processors, with a rate of 57,040 bases per second. In addition, our implementation uses up to 90.27\% less energy compared to the original implementation., Computer Engineering

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., Accepted author manuscript, Distributed Systems

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., Accepted author manuscript, Data-Intensive Systems