Category Archives: Transprecision

Open Position for Post-Doctoral Researcher on transprecise scheduling of machine learning tasks in edge and IoT environments

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We are currently seeking to appoint an exceptional candidate to the post of Research Fellow.

The post holder will perform research on deployment of machine-learned models for health analytics on distributed IoT/edge/cloud systems using transprecise computing and contribute to the research project “Sustainable Wearable Edge InTelligence (SWEET)”.

The successful candidate must have, and your application should clearly demonstrate that you meet the following criteria:

  • Normally have, or be about to obtain, a relevant PhD. Relevant areas include high-performance computing, middleware and computing systems.
  • Recent relevant research experience to include:
    • Undertaking research in the area of high-performance / distributed / parallel computing or middleware
    • A proven track record of using experimental models to carry out analyses, critical evaluations, and interpretations of experimental data as relevant to the research project
    • Working effectively as part of a research team in the development and promotion of the research theme.
    • Strong publication record commensurate with stage of career.

Please note the above are not an exhaustive list. For further information about the role including the essential and desirable criteria please check the recruitment web page.

This post is available on a fixed term contract for 33 months.

PhD Scholarship on Sustainable Wearable Edge InTelligence

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We are seeking to recruit a an excellent PhD candidate on the project “SWEET: Hardware and Software for Sustainable Wearable Edge InTelligence”, seeking to optimise performance and energy efficiency of machine learning inference in response to time-varying conditions.

Interested applicants can apply here: https://www.qub.ac.uk/courses/postgraduate-research/phd-opportunities/optimising-speed-energy-and-quality-of-machine-learning-models-with-transprecise-computing.html

Sustainable Wearable EdgE inTelligence (SWEET)

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The SWEET project will investigate the efficient deployment and sustainability issues of wearable sensors in particular for health analytics. Real-time remote monitoring of physiological indicators can support early detection and intervention in heart diseases and save lives. These services however require wearable technologies with strong predictive abilities, fast networks and fast servers to extract insights from the collected data. Unfortunately, these technology components are often inaccessible to hundreds of millions of people, specifically, people who live in areas with limited broadband connectivity and limited means to invest in local computing and communication infrastructure.

The project will focus on three components (i) energy-efficient wearable hardware accelerators using custom instruction set acceleration, (ii) distributed scheduling and machine learning model serving to account for performance variability of the systems and networks, (iii) technologies for efficient and portable deployment of web services and approximate key caching.

The project will support one PhD student and one post-doctoral researcher in our group.

The project is a collaboration between Deepu John (University College Dublin), Dimitrios S. Nikolopoulos (Virginia Tech), Bo Ji (Virginia Tech) and ourselves in DIPSA (Queen’s University Belfast), and is funded through the tripartite US-Ireland funding scheme.

We graciously acknowledge the support by the Department for the Economy, NI (contracts to be finalised).

Half-Precision Floating-Point Formats for PageRank: Opportunities and Challenges

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https://doi.org/10.1109/HPEC43674.2020.9286179

Mixed-precision computation has been proposed as a means to accelerate iterative algorithms as it can reduce the memory bandwidth and cache effectiveness. This paper aims for further memory traffic reduction via introducing new half-precision (16 bit) data formats customized for PageRank. We develop two formats. A first format builds on the observation that the exponents of about 99% of PageRank values are tightly distributed around the exponent of the inverse of the number of vertices. A second format builds on the observation that 6 exponent bits are sufficient to capture the full dynamic range of PageRank values. Our floating-point formats provide less precision compared to standard IEEE 754 formats, but sufficient dynamic range for PageRank. The experimental results on various size graphs show that the proposed formats can achieve an accuracy of 1e-4., which is an improvement over the state of the art. Due to random memory access patterns in the algorithm, performance improvements over our highly tuned baseline are 1.5% at best.