Deep Learning in the Automotive Industry


Embedl's Model Optimization SDK offers several benefits to companies in the automotive industry. These benefits include:

  1. Cost Reduction: By reducing the size of the model and reducing the power consumption and memory usage of the system, Embedl's Model Optimization SDK helps reduce the unit cost of manufacturing next-gen safety and convenience systems.
  2. Faster Time to Market: With Embedl's Model Optimization SDK, engineers can focus on building state-of-the-art models and be more flexible when it comes to what SoC to use. This, in turn, helps reduce the time to market for new products.
  3. Improved System Performance: By optimizing the code and reducing the size of the model, Embedl's Model Optimization SDK can improve the overall performance of the system, making it more efficient and reliable.
  4. Reduced Maintenance: With Embedl's Model Optimization SDK, engineers will spend less time on optimization and maintenance and more time building state-of-the-art models.

The tools provided are completely automated, ensuring that the process of experimentation is significantly reduced in terms of time consumption. This directly leads to a shortened time-to-market, allowing for quicker implementation and a competitive edge in the industry. An additional benefit is that these tools liberate data scientists from the burden of repetitive tasks, enabling them to allocate their valuable time and expertise to tackle the core problems at hand. With these automated tools, data scientists can delve deeper into data analysis, algorithm development, and model refinement, enhancing overall efficiency and productivity. By streamlining processes and optimizing resource allocation, organizations can optimize their operations, make data-driven decisions, and ultimately drive successful outcomes.


The Embedl Optimization Engine is a groundbreaking technology that streamlines the deployment of models in resource-constrained environments, like embedded systems. One of its key features is the automatic reduction of weights, which effectively decreases the model's overall size. This reduction in size is crucial for seamless integration into devices with limited resources, allowing for efficient and effective performance. By optimizing the model's weight distribution, the Embedl Optimization Engine ensures that it remains compatible with embedded systems without compromising its functionality. This innovative engine represents a significant breakthrough in the field of machine learning, as it overcomes the challenges posed by limited resources and enables the successful deployment of advanced models in various applications.


By implementing advanced techniques to fine-tune the Deep Learning model, it becomes feasible to identify cost-effective hardware solutions that align with the specific system requirements, thereby enhancing the profit margins of your products. This optimization process involves enhancing the performance, efficiency, and accuracy of the model by leveraging cutting-edge methodologies. By leveraging these advancements, it is possible to identify suitable hardware options that provide the necessary computational power and storage capabilities while being available at more affordable prices. This approach enables businesses to strike a balance between their performance needs and budget constraints, allowing them to save significant costs on hardware acquisition. 

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Energy is a precious and scarce resource in the world of embedded systems, making it absolutely essential to discover efficient methods to optimize its consumption. With our cutting-edge optimizer, we are able to achieve a remarkable decrease in energy usage when executing Deep Learning models. Through meticulous analysis and fine-tuning of the many components within these models, we can pinpoint and eliminate any unnecessary processes that consume energy. By implementing our optimizer, energy consumption can be effectively reduced by a magnitude, ensuring that embedded systems can operate in a sustainable and highly efficient manner.

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By implementing cutting-edge techniques to optimize Deep Neural Networks, we can successfully reduce the time it takes for your system to execute tasks, thereby assisting you in meeting your real-time requirements. Our state-of-the-art methods leverage advanced algorithms and finely tuned parameters to maximize the efficiency of your neural network, resulting in a substantial decrease in execution time. This optimization process is meticulously designed to address the specific needs of your application, ensuring that it operates seamlessly within the desired time constraints. With our expertise in developing and fine-tuning deep learning models, we can help you achieve exceptional performance and responsiveness, enabling you to fulfill your real-time objectives efficiently.


At our company, we specialize in optimizing and deploying customers’ Deep Learning models to embedded systems. By entrusting this task to our skilled team, you can relieve the burden from your own staff and allow them to concentrate on tackling your primary challenges. Our expertise lies in streamlining the implementation process for these complex models, ensuring they are effectively utilized within your embedded systems. With our assistance, you can rest assured that your Deep Learning models will be seamlessly integrated, resulting in enhanced functionality and improved performance. Let us handle the intricacies of optimizing and deploying your models, while you stay focused on addressing the core issues that matter most to your business.

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