Gothenburg-based deeptech company Embedl has raised €5.5 million in a pre-Series A funding round from Chalmers Ventures, Fairpoint Capital, SEB Greentech, Spintop Ventures, STOAF and Almi Invest. A spinout from Chalmers University of Technology, Embedl develops software that improves the efficiency of AI inference on embedded systems—reducing energy use and operational costs in sectors such as defense, automotive, and robotics. The company plans to use the funding to further develop and commercialise its SaaS platform, Embedl Hub, which helps manufacturers deploy AI models directly onto hardware with minimal resource consumption. The round supports growing industry demand for edge AI solutions that do not rely on cloud infrastructure and can operate within strict performance and energy constraints.
Embedl focuses on optimizing artificial intelligence models for deployment on embedded systems. Originating as a spinout from Chalmers University of Technology, the company develops tools that reduce the computational and energy demands of AI inference, enabling applications in sectors such as automotive, robotics, and defense. Embedl’s products are designed to support real-time AI processing on hardware with limited resources, without relying on cloud connectivity. Its technology is based on academic research in machine learning and model compression, and is used by both startups and large industrial firms.
“The world needs to make AI more energy efficient, fast. While the applications and usage of AI continue to skyrocket, we can’t increase energy consumption at the same level. Our solution will also help bring robotics and autonomous vehicles to the market faster, as we can help optimise the hardware’s energy efficiency while assuring the highest quality data being transferred instantly. We are grateful for the new and existing investors for their support,” says Hans Salomonsson, CEO and co-founder of Embedl.
Inference of AI models surpassed AI training costs in 2024 and is still projected to continue increasing. As more and more original equipment manufacturers (OEMs) add AI features to their products, the need to run inference on low-energy and cost-efficient devices in real-time is increasing. Companies are looking for solutions that ensure their AI inference works seamlessly, even without cloud support.
Embedl’s proprietary technology enables companies from the defense, automotive, and robotics sectors to transfer their deep learning models, convolutional neural networks (CNNs), and transformer models into their hardware devices. Embedl’s technology can reduce the energy consumption up to 83%, and manufacturers can halve the cost of their hardware by optimising their models.
“Having the ability to deeply inspect the cognitive blocks of our AI models, perform hardware-aware optimization, benchmark various layers, and deploy models through seamless hardware abstraction is truly game-changing,” said Shubham Shrivastava, Head of Machine Learning at Kodiak.
For example, the defense industry relies on highly secure and efficient technologies to maintain operational superiority and readiness. The devices used need to have optimal battery life, and sensitive information cannot always be sent to the cloud for analysis.
Embedl’s Model Optimization SDK helps AI systems in defense run efficiently on existing hardware, avoiding costly upgrades. It offers tools to prune, quantise, and compress deep learning models, reducing size and speeding up inference. Its modular design lets developers tailor components for specific needs and apply their domain knowledge. Built-in visualisation tools make it easy to track model changes during optimisation.
The automotive industry has been at the forefront in developing cutting-edge safety-critical functions, which require the utilization of cost-efficient hardware. In order to remain profitable and competitive, companies are constantly seeking methods of reducing manufacturing costs. Embedl’s edge AI tools can effortlessly deploy generative AI models across multiple hardware platforms.
“This funding is a sign that Chalmers has the technical expertise to build great AI solutions. We at Chalmers Ventures are proud to continue backing our portfolio companies that deliver, and we expect great things from Embedl, in addition to the impressive achievements they have already made in such a short time,” says Jonas Bergman, Investment Director at Chalmers Ventures.
Embedl has been listed as one of the most promising startups by CB Insights’ AI100 list, NyTeknik’s 33 List, and it has won The Grand Prize for Engineering, and IVA’s Smart Industry 2024 award.
The technology is based on research by Professor Devdatt Dubashi, Data Science and AI, Computer Science and Engineering, Chalmers University of Technology.
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Read the orginal article: https://arcticstartup.com/embedl-raises-e5-5m-pre-series-a/
