Embedl
Hannes von Essen has worked as a Deep Learning Researcher at EmbeDL since February 2021. Prior to that, they were a Research Assistant at Chalmers tekniska högskola from July 2020 to December 2020. Hannes also completed a Master Thesis Project at Whywaste from January 2020 to June 2020.
Hannes von Essen completed their Master's degree at Chalmers University of Technology from 2018 to 2020. Hannes focused on Complex Adaptive Systems during their studies. Prior to that, from 2015 to 2018, Hannes also earned their Bachelor's degree in Software Engineering from the same university.
Embedl
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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.