Modernise Compiler Technology with Deep Learning

Compilers are integral to our computing infrastructure, translating high-level source code into low-level machine instructions. Their efficiency directly influences computing performance, environmental impact, and user experience. However, today's compilers still rely on techniques developed several decades ago. They are limited by many sub-optimal choices used to work around the constraints of computers designed 30 years ago. This results in poor performance and energy inefficiencies, impacting the user experience and hindering scientific discovery and business innovation.<br /> <br /> This project seeks to bring compiler technology to the 21st century to allow compilers to take advantage of machine learning (ML), artificial intelligence (AI) techniques, and modern computing hardware. Our goal is to reduce human involvement in developing compiler optimisations so that compilers can quickly catch up with the ever-changing hardware and deliver scalable performance on current and future computing hardware. <br /> <br /> This idea of entirely relying on ML to learn code analysis and optimisation strategies is highly ambitious. However, the recent breakthrough effectiveness of ML in domains like game playing, natural language processing, drug discovery, chip design, and autonomous systems gives us the confidence that this is now possible in compilers. If AI can learn to drive a car, it must be able to reason about programs to perform optimisations like scheduling machine instructions.<br /> <br /> If successful, this project will fundamentally change how compilers are designed and optimised. Compilers can deliver optimal performance on any hardware architecture while keeping pace with technological advancements. Programs will be run faster and save more energy than is currently possible. Our group, which has pioneered using ML to optimise compilers and is supported by strong industry collaborations, is ideal to host the PhD project.<br />

<p>Compilers are integral to our computing infrastructure, translating high-level source code into low-level machine instructions. Their efficiency directly influences computing performance, environmental impact, and user experience. However, today's compilers still rely on techniques developed several decades ago. They are limited by many sub-optimal choices used to work around the constraints of computers designed 30 years ago. This results in poor performance and energy inefficiencies, impacting the user experience and hindering scientific discovery and business innovation.</p> <p>This project seeks to bring compiler technology to the 21st century to allow compilers to take advantage of machine learning (ML), artificial intelligence (AI) techniques, and modern computing hardware. Our goal is to reduce human involvement in developing compiler optimisations so that compilers can quickly catch up with the ever-changing hardware and deliver scalable performance on current and future computing hardware. </p> <p>This idea of entirely relying on ML to learn code analysis and optimisation strategies is highly ambitious. However, the recent breakthrough in the effectiveness of ML in domains like game playing, natural language processing, drug discovery, chip design, and autonomous systems gives us the confidence that this is now possible in compilers. If AI can learn to drive a car, it must be able to reason about programs to perform optimisations like scheduling machine instructions.</p> <p>If successful, this project will fundamentally change how compilers are designed and optimised. Compilers can deliver optimal performance on any hardware architecture while keeping pace with technological advancements. Programs will be run faster and save more energy than is currently possible. Our group, which has pioneered using ML to optimise compilers and is supported by strong industry collaborations, is ideal to host the PhD project.<br />  </p>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Computer Science FT</strong></em> and in the research information section that the research degree you wish to be considered for is <em><strong>Modernise Compiler Technology with Deep Learning</strong></em> as well as Professor <a href="https://eps.leeds.ac.uk/computing/staff/6452/dr-zheng-wang">Zheng Wang</a> as your proposed supervisor and in the finance section, please state clearly <em><strong>the funding that you are applying for, if you are self-funding or externally sponsored</strong></em>.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p style="margin-bottom:11px"><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p class="MsoNoSpacing" style="text-align:start; margin-bottom:24px">Applications will be considered after the closing date.  Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p style="margin-bottom:11px"><strong>Please note that you must provide the following documents in support of your application by the closing date of Monday 6 January 2025 if applying for the China Scholarship Council-University of Leeds Scholarship, Monday 3 February 2025 if applying for Leeds Doctoral Scholarship or Tuesday 1 April 2025 for Leeds Opportunity Research Scholarship.</strong></p> <p>If you are applying with external sponsorship or you are funding your own study, please ensure you provide your supporting documents at the point you submit your application:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.5 overall with at least 6.5 in writing and at 6.0 in reading, listening and speaking or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p style="margin-bottom:11px"><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>UK</strong> – The <a href="http://phd.leeds.ac.uk/funding/138-leeds-doctoral-scholarship-2025-faculty-of-engineering-and-physical-sciences#:~:text=Key%20facts&text=One%20Leeds%20Doctoral%20Scholarship%20is,rata%20for%20part%2Dtime%20study.">Leeds Doctoral Scholarships</a> <strong>(closing date: Monday 3 February 2025)</strong> and <a href="https://phd.leeds.ac.uk/funding/234-leeds-opportunity-research-scholarship-2022">Leeds Opportunity Research Scholarship</a> <strong>(closing date: Tuesday 1 April 2025)</strong> are available to UK applicants.  <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK</strong> – The <a href="https://phd.leeds.ac.uk/funding/48-china-scholarship-council-university-of-leeds-scholarships-2021">China Scholarship Council - University of Leeds Scholarship</a> is available to nationals of China <strong>(closing date: Monday 6 January 2025)</strong>. The <a href="https://phd.leeds.ac.uk/funding/73-leeds-marshall-scholarship">Leeds Marshall Scholarship</a> is available to support US citizens. <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p>You will be responsible for paying the overtime fee in full in your writing up/overtime year (£320 in Session 2024/25), but the scholarship maintenance allowance will continue to be paid for up to 6 months in the final year of award.</p> <p><strong>Important:</strong> Please note that that the award does <em><strong>not</strong></em> cover the costs associated with moving to the UK.  All such costs (<a href="https://www.leeds.ac.uk/international-visas-immigration/doc/applying-student-visa">visa, Immigration Health Surcharge</a>, flights etc) would have to be met by yourself, or you will need to find an alternative funding source. </p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

<p>For further information about your application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p> <p>For further information about this project, please contact Prof. Zheng Wang by email to <a href="mailto:Z.Wang5@leeds.ac.uk">Z.Wang5@leeds.ac.uk</a></p>