NEWS

Big data analytics provided by latest Ansys architecture

Ansys, a provider of engineering simulation technology, has announced the release of its SeaScape architecture to help engineers accelerate the optimisation of designs using a combination of elastic computation, machine learning, big data analytics and simulation technology.

Available today, the first product available as part of the Ansys SeaScape architecture is Ansys SeaHawk, which is aimed at engineers designing integrated circuits.

‘Die size and development time reduction are targets that electronic design engineers have pursued with marginal success given the limitations of today's in-design solutions,’ said John Lee, general manager, Ansys. ‘Ansys SeaHawk bridges the in-design and sign-off needs by bringing unprecedented simulation performance and design insights without sacrificing sign-off accuracy and coverage. We're excited to offer SeaHawk to the EDA industry today and equally excited to offer other SeaScape-based products across our entire simulation portfolio in the future.’

Engineering simulation generates tremendous amounts of data – far more than most organisations can effectively leverage for future product designs. A typical integrated circuit, for example, has billions of variables that can be simulated.

In many cases this means that engineering supercomputing resources are not keeping pace with the demand for increasingly complex simulations. By making effective use of big data technologies as elastic compute and map reduce, SeaScape provides an infrastructure to address these issues – with he hope of accelerating the pace of innovation during the design process.

The first product on the SeaScape infrastructure, SeaHawk, transforms electronic product design through significant improvements in simulation coverage, turnaround times and analysis flexibility. The combination of big data techniques and Ansys simulation capabilities provides SeaHawk users with a broad range of capabilities to reduce size of the chip and its power consumption without sacrificing performance or schedule constraints.

These results provide more useful insight to product developers early in the design process so they can produce innovative designs quicker and more efficiently. Ansys has collaborated with Intel to optimise SeaScape to take full advantage of the many-core Intel Xeon processor and Intel Xeon Phi processor families.

‘The performance increases Ansys SeaHawk delivers for engineering simulations enable users to freely optimize and innovate designs without constraints,’ said Hugo Saleh, director of marketing, High Performance Computing Platform Group at Intel Corporation. ‘The collaboration between Intel and Ansys continues to deliver innovation and performance for our respective customers, providing great value and performance for reduced time to results. Together with Ansys we're delivering leading simulation capabilities to market utilizing the Intel Scalable System Framework.’

Ansys reports that early users have achieved an average of five per cent reduction in die size, which could result in millions of dollars in savings during the course of production.

Twitter icon
Google icon
Del.icio.us icon
Digg icon
LinkedIn icon
Reddit icon
e-mail icon
Feature

Building a Smart Laboratory 2018 highlights the importance of adopting smart laboratory technology, as well as pointing out the challenges and pitfalls of the process

Feature

Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory

Feature

This chapter will consider the different classes of instruments and computerised instrument systems to be found in laboratories and the role they play in computerised experiments and sample processing – and the steady progress towards all-electronic laboratories.

Feature

This chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined-up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re-use of knowledge to develop understanding and to create value

Feature

This chapter takes the theme of knowledge management beyond document handling into the analysis and mining of data. Technology by itself is not enough – laboratory staff need to understand the output from the data analysis tools – and so data analytics must be considered holistically, starting with the design of the experiment