You need a strong background in Computer Science, experience with compilers and machine learning, ability to write code for tools like LLVM and DynamoRIO, and familiarity with engineering practices such as version control and automated testing.

You will conduct research in machine-learning guided compilation within the Computer Architecture Research Group, focusing on performance, reliability, and security in many-core systems. Collaborate with industry and academia to develop solutions for parallelism.

Data

Software Engineering

United Kingdom

Cambridge

Large

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Education

University of Cambridge

Research, news and events from the University of Cambridge

<ul><li>Applications are invited for a PhD student to work on machine-learning guided and verifiably correct code generation.</li><li>Creating optimised libraries is a difficult and time-consuming task, requiring significant manual engineering effort. This process must be repeated for each new processor to take advantage of additional features, especially when it implements the latest architecture with new instructions or significant new architectural extensions, like Arm's SVE and SME. However, advances in machine learning point towards a low-cost solution to this task by automating code generation through a series of provably correct steps. A machine-learning model will guide the search for optimised code sequences, learning the best instructions to use for given intermediate code fragments and alleviating manual engineering effort.</li><li>The successful candidate will develop new code-generation strategies using machine-learning models and verification tools, suitable for deployment by library writers within the compilation toolchain, working closely with project partner, Arm.</li></ul>

2:1 or higher

Masters

Applications are invited for a PhD student to work on machine-learning guided and verifiably correct code generation.Creating optimised libraries is a difficult and time-consuming task, requiring significant manual engineering effort. This process must be repeated for each new processor to take advantage of additional features, especially when it implements the latest architecture with new instructions or significant new architectural extensions, like Arm's SVE and SME. However, advances in machine learning point towards a low-cost solution to this task by automating code generation through a series of provably correct steps. A machine-learning model will guide the search for optimised code sequences, learning the best instructions to use for given intermediate code fragments and alleviating manual engineering effort.The successful candidate will develop new code-generation strategies using machine-learning models and verification tools, suitable for deployment by library writers within the compilation toolchain, working closely with project partner, Arm.

PhD Studentship in Machine-Learning Guided Compilation (Fixed Term)

Requirements

Responsibilities

University of Cambridge

Mission & Purpose