Tel Aviv-based startup Deci has secured $9.1m in seed funding to build an AI-based platform that can automatically optimise AI models for enterprises.
Venture capital firms Emerge and Square Peg led the funding round.
Deci’s platform specialises in deep neural network solutions – an AI model that emulates the neural networks of the human brain to train itself. Such models are used for driverless cars, facial recognition and medicine discovery.
Engineers can add their deep neural network solutions to Deci’s platform, which will then use AI to tweak and optimise the models to make them more efficient and ready for production at scale.
The aim is to make AI models more efficient and able to run on various types of hardware, whether it’s a GPU, CPU or specialised AI computer chip. This optimisation can cut computing costs and reduce the development lifecycle, the company claims.
The startup says its hardware optimisation could reduce the performance gap between CPUs and GPUs. This would bring deep learning capabilities to more CPUs, which are generally cheaper than GPUs.
Deci has applied for a patent for its Automated Neural Architecture Construction (AutoNAC) technology that underpins the platform.
Deci raised an undisclosed amount in seed funding in August last year and has partnered with companies developing autonomous vehicles, as well as those in healthcare, manufacturing and communication.
It was founded in 2019 by technology entrepreneur Jonathan Elial, computer scientist professor Ran El-Yaniv, deep learning scientist Yonatan Geifman.
“Deci is leading a paradigm shift in AI to empower data scientists and deep learning engineers with the tools needed to create and deploy effective and powerful solutions,” said Deci CEO and co-founder Yonatan Geifman.
“The rapidly increasing complexity and diversity of neural network models make it hard for companies to achieve top performance. We realised that the optimal strategy is to harness the AI itself to tackle this challenge. Using AI, Deci’s goal is to help every AI practitioner to solve the world’s most complex problems.”