Research into software for accelerating signal and data processing solutions has resulted in the development of state-of the-art data analytics software that leverages connected technology to deliver better insights and outcomes for users.
This technological breakthrough led to the creation of Analytics Engines, a spinout from Queen’s University which specialises in data integration, machine learning, AI, in-depth analytics and visualisations.
The bespoke software solutions can be applied across a diverse range of sectors including healthcare, trade, finance and digital infrastructure.
Research Challenge
HARNESSING NEW TECHNOLOGY TO OPTIMISE HARDWARE
Effectively harnessing new and evolving computing technologies is a key challenge for many signal and data processing applications.
A Field Programmable Gate Array (FPGA) is an integrated circuit that can be programmed by a user for a specific use after it has been manufactured. The main attraction of using novel computing technologies is that they offer a considerable cost benefit as users can optimise the hardware in a way not possible using conventional computing architectures.
FPGA companies create intellectual property (IP) cores that allow designers to take advantage of previously implemented designs. However, a significant bottleneck existed that whilst companies have system models, IP cores and FPGA boards, designers still need to spend several months achieving an efficient implementation.
Innovative research from Queen’s successfully addressed this bottleneck and led to the creation of Analytics Engines.
Our Approach
A BREAKTHROUGH IN DATAFLOW PROGRAMMING
Researchers at Queen’s Led by Professor Roger Woods made key advances in this area of accelerated FPGA implementation that are now used by Analytics Engines. these breakthroughs set the company up for important early successes.
An innovative methodology based on a data flow programming approach led to a suite of prototype tools for rapid algorithmic to programme implementation on FPGA’s, thereby reducing non-recurrent engineering design time.
The work was matured through research funded by the UK Electro-Magnetic Remote Sensing Defence Technology Centre and resulted in the development of a prototype software framework called Owen for the rapid implementation and optimisation of Digital Signal Processing systems on FPGA platforms.
The work later resulted in the development of an FPGA-based solution for Real-time Analytics on Fast Data Streams. Recognising the value and potential benefit of this breakthrough for multiple industries, Analytics Engines Ltd was founded in 2014 to commercialise the research.
The firm’s solutions have allowed organisations to improve their data analytics capacity and to transform and modernise traditional processes, saving time and money for organisations in the areas of health care, medical science, trade, finance and digital infrastructure.
What impact did it make?
SAVING ORGANISATIONS TIME AND MONEY THOUGH AGILE DATA SOLUTIONS
Analytics Engines has created solutions used by over 30 organisations, delivering significant insights and successful outcomes for its customer including The National Gallery London, and RTE.
The firm’s solutions have allowed organisations to improve their data analytics capacity and to transform and modernise traditional processes, saving time and money for organisations in the areas of health care, medical science, trade, finance and digital infrastructure.
One early achievement was the development for SAP (a large German software company with 335,000 customers) of an FPGA-based acceleration solution which resulted in a 22 times speedup when integrated with SAP’s core product HANA, a relational database management system.
The Almac Group, a leading pharmaceutical research organization employing over 3,300 people, identified a bottleneck in their bioinformatics pipeline workflow, requiring a run time of 33 hours thereby limiting their diagnostic capability. Analytics Engines developed a solution which resulted in a 98% reduction in runtime.
Almac state, ‘The pipeline can now be set up to run with 15 minutes of hands on time and results returned within 3-4 hours. Depending on the complexity of the dataset and analysis, this saves 20-50 hours of FTE per analysis and enables Almac to take a data driven discovery approach. The pipelines are now run reliably, reproducibly and repeatably with a full audit trail’.
In July Analytics Engines became a key partner in Smart Nano NI – a £60m nano technology consortium which presents a game-changing opportunity for Northern Ireland’s nano technology and manufacturing sector.
Our impact
Impact related to the UN Sustainable Development Goals
Learn more about Queen’s University’s commitment to nurturing a culture of sustainability and achieving the Sustainable Development Goals (SDGs) through research and education.
