Introducing Embedl Model Optimization SDK, the groundbreaking solution that brings efficient deep learning to embedded systems. Our cutting-edge tools empower you to optimize deep neural networks, ensuring peak performance on your target constrained hardware. Say goodbye to limitations and embrace the future of AI fully optimized!
we specialize in
Model Optimization
At the heart of Embedl's mission lies model optimization, a sophisticated process where we specialize in crafting cutting-edge software tools that automate the optimization of neural network models.
The processing time of your final product is guaranteed to outperform all expectations with the aid of Embedl's exceptional capabilities. Utilizing Embedl’s Model Optimization SDK also significantly reduces engineering time, leading to faster time-to-market for your projects. Allowing your team of DL-experts to focus on solving core business problems.
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Tailor and build a model
Optimize for any hardware target
One of the distinct features that sets Embedl apart is our ability to optimize any model for any hardware target, which we proudly refer to as "hardware aware model optimization." This approach ensures optimal utilization and maximum performance for each hardware, it also enables our customers to support multiple hardware with a single source model.
Customers have also embraced Embedl for their hardware evaluation needs. Embedl allows you to quickly evaluate how fast a production version of the model will execute on a range of hardware.
Our contributions extend beyond software alone. Our valued customers greatly appreciate our unique approach to disseminating state-of-the-art expertise in the research domain. Our goal is to provide unwavering support for any target hardware platform, revolutionizing the way we optimize and leverage neural network models.
Technology
Model Optimization SDK
Our award winning Model Optimization SDK optimizes your Deep Learning model for deployment (inference) to meet your requirements of:
- Execution Time (Latency)
- Throughput
- Runtime Memory Usage
- Power Consumption
Embedl enables you to deploy Deep Learning on less expensive hardware, using less energy, and significantly shorten the product development cycle. With our cutting-edge Model Optimization SDK, you can optimize your Deep Learning model for seamless deployment (inference) that perfectly aligns with your specific requirements.
By leveraging Embedl, you can achieve exceptional results across various performance metrics. Whether you prioritize execution time (latency), throughput, runtime memory usage, or power consumption, our award-winning Model Optimization SDK ensures that your Deep Learning model operates at its peak efficiency.
What sets Embedl apart is its seamless integration with the most widely used Deep Learning development frameworks, such as Tensorflow, Keras and Pytorch. This enables you to seamlessly transition your models and leverage the full power of Embedl without any hassle or compatibility issues.
Embedl boasts world-leading support for a wide range of hardware targets. From CPUs to GPUs, FPGAs to ASICs, our Model Optimization SDK is built to work flawlessly with hardware from renowned vendors like Nvidia, ARM, Intel, and Xilinx. This allows you to choose the hardware that best suits your needs and ensures optimal performance and efficiency.
Why Choose Embedl?
Utilizing Embedl’s Model Optimization SDK leads to...
Faster Execution
By using state-of-the-art methods for optimizing Deep Neural Networks, we can achieve a significant decrease in execution time and help you reach your real time requirements.
Less Energy Usage
Energy is a scarce resource in embedded systems and our optimizer can achieve an order of magnitude reduction in energy consumption for the Deep Learning model execution.
Improved Product Margins
By optimizing the Deep Learning model, cheaper hardware can be sourced that still meets your system requirements leading to improved product margins.
Shorter Time-To-Market
The tools are fully automatic, which reduces the need for time consuming experimentation and thus shorter time-to-market. It also frees up your data scientists to focus on their core problems.
Smaller Footprint in Device
The Embedl Optimization Engine automatically reduces the number of weights , and thus size of the model, to make it suitable to be deployed to resource constraint environments such as embedded systems.
Decreased Product Risk
Optimizing and deploying our customers’ Deep Learning models to embedded systems is what we do. By outsourcing this to us, your team can then focus on your core problems.
"We understand that every Deep Learning project is unique, and that's why we offer personalized assistance. Our team is dedicated to answering any questions you may have and is more than happy to provide a demonstration of Embedl with your Deep Learning model(s). Experience the power and efficiency of Embedl first-hand and unlock the full potential of your models."
Hans Salomonsen. CEO
Don't wait! Let's do it together!
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