Embedl offers a diverse set of cutting-edge tools and expertise, empowering AI teams to craft incredibly efficient Deep Learning models. Embedl aims to revolutionize the world of embedded systems by commercializing its state-of-the-art Deep Learning Model Optimization SDK. With Up to 94% reduced energy consumption, 16x increased execution speed, and 8x memory model compression, the SDK enables users to develop market-leading solutions by designing energy-efficient deep learning models that meet real-time requirements in cost-efficient hardware. Through the innovative Model Optimization SDK, the process of generating superior models becomes streamlined and automated.
Embedl is committed to innovation and excellence, is deeply connected to academic research, and actively collaborates with prominent industry players. These valuable collaborations nurture a thriving innovation ecosystem, empowering Embedl to constantly push the limits of what's achievable in embedded intelligence.

AI/ML

Embedl

Hans Salomonsson

Peter Kristiansen

Ola Tiverman

Frida D.

Devdatt Dubhashi

The Deep Learning Team at Embedl focuses on researching, developing, and optimizing deep learning models to achieve peak efficiency in embedded systems. This team leverages their expertise to innovate and refine the Model Optimization SDK, significantly reducing energy consumption while enhancing execution speed and memory compression. They work closely with academic institutions and industry partners to ensure that Embedl's solutions remain at the forefront of technological advancements in embedded intelligence.

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Deep Learning Team

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Deep Learning Team

About

Team members

Other teams at Embedl

Leadership Team

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