AutoSens and InCabin | 21-23 MAY 2024 | HUNTINGTON PLACE, DETROIT, USA

Come visit us at our booth 119. We are set to present our hardware-aware model optimization software development kit (SDK). This advanced toolkit has been meticulously designed to enhance the efficiency and performance of deep learning models by optimizing them for specific hardware configurations. Our team of experts has leveraged cutting-edge technology to ensure that this SDK stands at the forefront of innovation, providing unparalleled benefits in terms of speed and reliability. We are committed to pushing the boundaries of what is possible in the realm of model optimization, and we look forward to sharing our findings with the community at AutoSens and InCabin. 

Find more information about the event here:


Our Deep Learning Researcher Andreas Ask is taking the stage

Andreas Ask, Embedl-min


In the rapidly evolving landscape of Advanced Driver Assistance Systems (ADAS) and autonomous driving technologies, Embedl, in partnership with Zenseact, is setting new benchmarks. Our collaboration signifies a leap forward in enhancing autonomous vehicles' safety, efficiency, and reliability. Through our joint efforts, we're developing technology and crafting the future of transportation.

Empowering ADAS with Deep Learning Innovations

At the core of our advancements lies a deep understanding of the intrinsic challenges associated with ADAS and autonomous driving systems. The complexity of real-world scenarios demands solutions that are not just innovative but also practical and scalable. Our deep learning researcher, Andreas Ask, is at the forefront of this revolution, showcasing our latest developments at Autosens May 22nd at 11:15am EDT.


Addressing the Efficiency Paradigm

Our presentation, "An Efficient ADAS Stack: Integrating Multi Task Models, Efficient Implementations, and Hardware-Aware Optimization," dives deep into the essence of our approach. It's a testament to our commitment to tackling the threefold challenge of memory management, energy consumption, and computational efficiency in deep learning models. Our methodology underscores a pivotal realization: the path to success in autonomous driving technologies follows a variety of strategies., It requires a nuanced, iterative process of innovation driven by the expertise of specialists and engineers.

Innovative Strategies for Streamlined Deep Learning Applications

Zenseact employs multiple strategies to refine deep learning applications for ADAS and autonomous driving. One of the cornerstones is the development of custom C++ and CUDA implementations. This ensures fast execution of deep learning models, making them more suitable for real-time applications in autonomous vehicles.

Furthermore, multitask deep learning models are designed to combine critical tasks such as semantic segmentation and lane detection into a single, cohesive network. This integration not only reduces computational redundancy but also optimizes the utilization of available resources, thereby enhancing the system's overall efficiency.

Hardware-Aware Model Optimization

A critical element in creating an efficient ADAS stack is hardware-aware deep learning model optimization. Leveraging the Embedl Model Optimization SDK, we've been able to tailor model architectures to exploit the capabilities of specific hardware configurations fully. This optimization process results in significant gains in efficiency and execution speed, ensuring autonomous driving systems that are not just effective but also scalable and adaptable to various hardware ecosystems and deployment pipelines.

A Special Focus on Model Optimization

Our talk at the InCabin & Autosens event places a particular emphasis on model optimization. It addresses one of the most significant hurdles in the deployment of ADAS and autonomous driving technologies: the need for models that are not just accurate but also efficient and responsive to the real-time demands of autonomous driving. Through our presentation, we aim to share insights into our cutting-edge strategies and their impact on the development of ADAS and autonomous driving technologies.

Looking Forward

The journey toward fully autonomous driving is fraught with challenges but also filled with opportunities. Our work in enhancing the vision stack for ADAS and AD systems is a testament to our commitment to innovation, efficiency, and safety. Our efforts are geared towards creating a world where autonomous driving is not just a possibility but a reality that's safe and efficient.

As we look to the future, we focus on advancing the state of the art in ADAS and autonomous driving technologies. Our commitment to innovation and deep expertise in deep learning and model optimization positions us at the forefront in this exciting and transformative field. Together with Zenseact, we're not just imagining the future of transportation; we're actively creating it.

Will you be there? Let's connect! Contact us to schedule a meeting!

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