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Architecture changes to make spatially programmable hardware (FPGAs) more efficient and easier to use. A major focus in this area is creating new FPGA architectures that are more efficient for deep learning inference, yet still highly programmable and general purpose enough to implement entire systems. Another focus is creating FPGAs that are more datacenter-friendly, allowing easier design and simpler use by multiple applications through techniques like embedding NoCs in the fabric.
New Computer-Aided Design tools to make it easier to design hardware, and to investigate new FPGA architectures.
Methods to map deep learning applications to direct hardware execution on programmable devices like FPGAs. By generating customized hardware for each layer in a neural network we can outperform prior approaches, and by changing the chips themselves in our FPGA architecture research we can improve efficiency even more.
CAD tools to make FPGAs easier to debug and better suited to the data center; in particular we are seeking ways to let FPGA tasked be interrupted and safely context switched in and out of hardware in a data center.
Hardware acceleration of important problems and software tools to optimize medical treatments; most recently I've focused on simulating photon scattering in complex human tissue to aid photodynamic cancer treatments. This is a form of light-activated chemotherapy which can better target tumours than conventional chemotherapy, but which requires advanced computation to determine where the fiber optic light probes should be placed (via hyperdermic needles) to achieve the best results.
Architecture changes to make spatially programmable hardware (FPGAs) more efficient and easier to use. A major focus in this area is creating new FPGA architectures that are more efficient for deep learning inference, yet still highly programmable and general purpose enough to implement entire systems. Another focus is creating FPGAs that are more datacenter-friendly, allowing easier design and simpler use by multiple applications through techniques like embedding NoCs in the fabric.
New Computer-Aided Design tools to make it easier to design hardware, and to investigate new FPGA architectures.
Methods to map deep learning applications to direct hardware execution on programmable devices like FPGAs. By generating customized hardware for each layer in a neural network we can outperform prior approaches, and by changing the chips themselves in our FPGA architecture research we can improve efficiency even more.
CAD tools to make FPGAs easier to debug and better suited to the data center; in particular we are seeking ways to let FPGA tasked be interrupted and safely context switched in and out of hardware in a data center.
Hardware acceleration of important problems and software tools to optimize medical treatments; most recently I've focused on simulating photon scattering in complex human tissue to aid photodynamic cancer treatments. This is a form of light-activated chemotherapy which can better target tumours than conventional chemotherapy, but which requires advanced computation to determine where the fiber optic light probes should be placed (via hyperdermic needles) to achieve the best results.
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ACM Transactions on Reconfigurable Technology and Systemsno. 1 (2024): 1-20
2023 33rd International Conference on Field-Programmable Logic and Applications (FPL)pp.72-78, (2023)
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2023 INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE TECHNOLOGY, ICFPTpp.152-160, (2023)
FCCMpp.41-51, (2023)
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2023 33rd International Conference on Field-Programmable Logic and Applications (FPL)pp.265-270, (2023)
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2023 INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE TECHNOLOGY, ICFPTpp.132-141, (2023)
THE PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM ON HIGHLY EFFICIENT ACCELERATORS AND RECONFIGURABLE TECHNOLOGIES, HEART 2023pp.11-18, (2023)
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