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职业迁徙
个人简介
The primary research questions I am interested in are: (i) how can compiler technology best exploit the potential of high performance heterogeneous architectures (ii) how can we best design high performance heterogeneous to meet emerging applications.
My research interests include:
Heterogeneous code discovery and optimisation. I am interested in how we can use diverse techniques including constraint analysis, program synthesis and machine learning to discover code patterns that match heterogeneous hardware and automatically tune them.
Deep Neural Network system stack. I currently investigate how to optimise deep learning inference based on real world device constraints. I am interested in a cross-stack approach incorporating Bayesian Optimisation at the model layer and a device specific optimisations at the hardware layer
Software Defined Hardware. I currently investigate how to map languages such as Python to flexible hardware (e.g. coarse-grain reconfigurable architectures). I am interested in both how software may be adapted to fit hardware and hardware reshaped for software.
Auto-parallelising compilers. I am currently investigating the use of dynamic, probabilistic analysis in conjunction with machine learning to develop a new approach that gives future proof scalable code for multi-cores.
GPGPU multi-core platforms. I am interested in how we can use smart compiler analysis and adaptation to exploit the potential of such architectures for compute and graphics workloads.
Machine learning based optimisation. I'm interested in how predictive modelling and feature generation can be used to automate the design of compilers
Compiler/Architecure co-design space exploration. We are investigating the use of predictive models to predict the best compiler optimisations for any architecture
Very High level programming languages. I am interested in how languages such as Matlab, which provide great expressive power may be implemented on multi-cores
My research interests include:
Heterogeneous code discovery and optimisation. I am interested in how we can use diverse techniques including constraint analysis, program synthesis and machine learning to discover code patterns that match heterogeneous hardware and automatically tune them.
Deep Neural Network system stack. I currently investigate how to optimise deep learning inference based on real world device constraints. I am interested in a cross-stack approach incorporating Bayesian Optimisation at the model layer and a device specific optimisations at the hardware layer
Software Defined Hardware. I currently investigate how to map languages such as Python to flexible hardware (e.g. coarse-grain reconfigurable architectures). I am interested in both how software may be adapted to fit hardware and hardware reshaped for software.
Auto-parallelising compilers. I am currently investigating the use of dynamic, probabilistic analysis in conjunction with machine learning to develop a new approach that gives future proof scalable code for multi-cores.
GPGPU multi-core platforms. I am interested in how we can use smart compiler analysis and adaptation to exploit the potential of such architectures for compute and graphics workloads.
Machine learning based optimisation. I'm interested in how predictive modelling and feature generation can be used to automate the design of compilers
Compiler/Architecure co-design space exploration. We are investigating the use of predictive models to predict the best compiler optimisations for any architecture
Very High level programming languages. I am interested in how languages such as Matlab, which provide great expressive power may be implemented on multi-cores
研究兴趣
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PROCEEDINGS OF THE 2ND ACM WORKSHOP ON SUSTAINABLE COMPUTER SYSTEMS, HOTCARBON 2023pp.20:1-20:6, (2023)
2023 32ND INTERNATIONAL CONFERENCE ON PARALLEL ARCHITECTURES AND COMPILATION TECHNIQUES, PACT (2023): 39-50
PROCEEDINGS OF THE 32ND ACM SIGPLAN INTERNATIONAL CONFERENCE ON COMPILER CONSTRUCTION, CC 2023pp.156-167, (2023)
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PROCEEDINGS OF THE 22ND ACM SIGPLAN INTERNATIONAL CONFERENCE ON GENERATIVE PROGRAMMING: CONCEPTS AND EXPERIENCES, GPCE 2023pp.42-56, (2023)
International Conference on Functional Programmingpp.81-94, (2022)
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