Silexica raises $18m in Series B funding to advance software development solutions for autonomous driving
- Series B led by EQT Ventures
- Investment will fund the further development of a Simulation Platform for software developers in multi-partner projects for embedded supercomputers
- Participation from all existing investors: Merus Capital, Paua Ventures, Seed Fonds Aachen and DSA Invest
Cologne, June 21, 2018 – Silexica, (silexica.com) the developer of the SLX programming tools has announced the completion of its $18 million Series B round of financing. The EQT Ventures Fund (EQT Ventures) led the round, joining all existing investors Merus Capital, Paua Ventures, Seed Fonds Aachen and DSA Invest.
The complexity of software design is evolving rapidly with embedded supercomputers required to deliver trillions of actions every second for systems such as autonomous cars. Due to the shift to the computing everywhere era with increased connectivity and rapid data processing, complex new computing architectures are becoming increasingly difficult to program manually.
Silexica has been developing the SLX programming tools since its launch in 2014 to analyze how software runs on these new heterogeneous multicore processors and provide deep system-level understanding for developers. Alternative approaches such as spreadsheet-based calculations are no longer capable of managing this new level of hardware complexity.
Silexica has expanded its presence with industry leading software and hardware experts. It has offices in Germany, the USA and Japan. Customers include Tier 1 automotive suppliers as well as leading OEMs in 5G, and Aerospace & Defense. For example, SLX has already helped Denso to automate workflows and migrate software to automotive multicore platforms and partners with Fujitsu on 4G/5G base-station projects.
Maximilian Odendahl, CEO of Silexica said: “We created SLX to support software professionals facing the biggest challenge in the industry – programming heterogeneous supercomputers. SLX is truly adding value to customers in delivering performance improvements and system insights on some of the most advanced computers being created. This funding round will enable us to strengthen existing and upcoming SLX tools and solutions. In addition, it will see us develop an industry first, vendor-neutral, multi-application Simulation Platform to help OEMs and all members of the supply chain optimize and integrate software provided for complex systems such as autonomous vehicles.”
EQT Ventures brings a network of leading industry experts and connections within the automation industry that, along with the investment, will continue to help Silexica grow and deliver solutions for the accelerating multicore development problem.
Ted Persson, Design Partner and investment advisor to EQT Ventures who will be joining Silexica’s Board, said: “In their quest to solve one of the largest challenges of the post-PC era, we believe Maximilian, Johannes and the rest of the team can steer Silexica into becoming one of the most important technology companies of this decade.”
The company’s $8 million Series A was completed in November 2016 and was led by Merus Capital and Paua Ventures with participation from existing investors Seed Fonds Aachen and DSA Invest.
For further information, to arrange an interview or for photographs, please email Gareth Beazant at email@example.com or telephone: +49 170 6207620.
Silexica provides software development solutions that enable technology companies to take intelligent products such as autonomous cars from concept to deployment. The SLX programming tools help developers implement software to run efficiently on embedded supercomputers by offering deep understanding of how software behaves on the system.
Silexica was founded in 2014 and has raised $28 million in funding. With headquarters in Germany and offices in the US and Japan, Silexica has partnered with global customers across many rapidly transforming industries including automotive, wireless and aerospace. For further information please visit: www.silexica.com