Hardware-Agnostic Deep Learning: Optimize, Adapt, and Deploy with Embedl Neural Compression SDK.


Design once - deploy everywhere

With Embedl's Neural Compression SDK, you can streamline the development process and focus your time and energy on designing a single model for your specific use case. Rather than creating multiple versions of your model to run on different hardware targets, Embedl's hardware-agnostic approach enables you to design once and deploy everywhere.

This means that you can develop a deep learning model that is optimized for your particular application, and Embedl's SDK will take care of optimizing it for a wide range of hardware targets, including CPUs, GPUs, FPGAs, and ASICs from vendors such as Nvidia, ARM, Intel, and Xilinx.

By using Embedl's SDK, you can ensure that your model performs optimally on any hardware target, regardless of its specifications. This not only saves time and resources but also ensures that your model can be easily deployed and scaled to meet the demands of your application.

In short, Embedl's "design once - deploy everywhere" approach enables you to focus on designing the best deep learning model for your use case, while the SDK takes care of the optimization and deployment process, resulting in faster time-to-market, improved efficiency, and increased scalability.



Hardware evaluation at scale

Optimizing deep learning models for accuracy, latency, and cost when comparing different potential hardwares can be a challenging task for developers. By providing a common abstraction layer agnostic to the type of hardware, Embedl enables hardware evaluation at scale. Combined with Embedl’s award winning Deep Learning Optimization Engine, this enables the exploration of the full landscape of latency, price, and accuracy trade-offs.

By exploring the latency-price-accuracy landscape, developers can discover the most efficient and cost-effective ways to achieve the desired level of accuracy, resulting in a more efficient and cost-effective deep learning model.

Automatic hardware adaptation

Embedl’s hardware support automatically adapts your model to each specific hardware, automatically replacing operations for compatibility and compressing the model to fit smaller devices, enabling the use of cheaper hardware.

This process reduces the memory and computational requirements of the model, enabling it to run efficiently on hardware with limited resources. By leveraging Embedl's automatic hardware adaptation capabilities, developers can deploy their models on a wider range of devices, including low-power embedded devices and mobile devices, without sacrificing accuracy or performance.

Hardware aware quantization

Embedl enables you to take full advantage of the float32, float16, int8, int4, 2-4 sparse, and mixed precision capabilities of each hardware, automatically choosing an optimal combination of different degrees of quantization for different parts of the model in a hardware aware manner through a hardware-aware sensitivity analysis.

This approach ensures that the model's precision is preserved while reducing the memory and computational requirements, resulting in faster inference times, reduced power consumption, and improved scalability. By taking full advantage of hardware-specific quantization capabilities, developers can achieve significant improvements in performance and efficiency without sacrificing accuracy.

Hardware aware neural architecture search

Through its wide hardware support, Embedl is able to evaluate the impact of architectural changes on the actual hardware latency, enabling the discovery of the unique leverage points that provide the most accuracy gain for the least latency cost. This is made possible by Embedl’s unique simulator-in-the-loop technique which speeds up the latency benchmarking of architectural changes by up to 100x compared to hardware-in-the-loop approaches.

In conclusion, Embedl's Neural Compression SDK offers a comprehensive solution to the challenges faced by deep learning developers when it comes to hardware optimization, deployment, and scaling, as well as both hardware evaluation and hardware adaptation. By providing a hardware-agnostic approach that automatically adapts models to specific hardware targets, developers can save time and resources while ensuring optimal performance. With hardware-aware quantization, neural architecture search, and the ability to explore the full landscape of latency, price, and accuracy trade-offs, developers can achieve significant improvements in performance and efficiency without sacrificing accuracy. By choosing Embedl's SDK, developers can streamline their deep learning development process and focus on designing the best model for their use case, resulting in faster time-to-market, improved efficiency, and increased scalability.

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