SMALLER FOOTPRINT IN DEVICE

One of the many challenging tasks when deploying deep learning models on resource-constrained devices such as embedded systems, mobile phones, or IoT devices is meeting the tight memory requirements of these environments.

With the help of the Embedl Optimization SDK, you can solve this and many other deployment problems when developing your AI-based products. By utilizing advanced deep learning model optimization techniques to automatically reduce the size of your models, we can drastically reduce their memory footprint, enabling them to run smoothly even on low-memory devices.

One of these key deep learning model optimization techniques is pruning. Pruning involves removing redundancy in your deep learning model by removing the weights and connections that contribute the least to its overall performance. By pruning your deep learning model, we can significantly reduce its memory usage, as its overall size is reduced.

A more advanced technique which goes one step further is one-shot neural architecture search. This technique can help you find the most efficient model architecture for a given task, while also minimizing the memory footprint of the model. By exploring a large number of potential architectures, you are given a wide array of options which meet your memory constraints and desired level of accuracy. This can save you a significant amount of time and resources that would otherwise be required to manually design and test different architectures.

By utilizing these techniques, you can deploy your deep learning models on devices which otherwise would have been costly in terms of time and resources without deep learning model optimization. It also opens up options for you as a developer to have more freedom in selecting hardware platforms with even tighter memory constraints, or allow the deployment of more complex models on your current choice of hardware. With our tools, you can reach the best desired trade-off of performance and cost tailored to your specific problem.