What is Roofline AI?
Edge AI is implemented with the help of a software development kit (SDK) called Roofline AI. It was developed by Roofline AI GmbH, a spin-off from RWTH Aachen University.
The following is made easier with RooflineAI’s SDK:
- Flexibility: Models from any AI framework, including ONNX, PyTorch, and TensorFlow, may be imported.
- Roofline AI provides excellent performance.
- Usability: RooflineAI is simple to use.
- RooflineAI makes it possible to deploy on a variety of hardware, such as CPUs, MPUs, MCUs, GPUs, and specialized AI hardware accelerators.
- RooflineAI’s retargetable AI compiler technology fosters collaborations with chip suppliers and the open-source community.
A computer science technique called the Roofline model aids programmers in figuring out a computation’s compute-memory ratio. It is employed to evaluate AI architectures’ memory bandwidth and computational efficiency.
To redefine edge AI deployment
Edge AI is developing quickly. Rapidly emerging novel models, like LLMs, make it difficult to foresee technological advancements. Simultaneously, hardware solutions are becoming more complicated and diverse.
Conventional deployment techniques are unable to keep up with this rate and have turned into significant obstacles to edge AI adoption. They are uncomfortable to use, have limited performance, and are not very adaptable.
With a software solution that provides unparalleled flexibility, superior performance, and user-friendliness, Roofline transforms this procedure. With a single Python line, import models from any framework and distribute them across various devices.
Benefits
Flexible
Install any model from any framework on various target devices. Innovative applications may be deployed on the most efficient hardware with to the retargetable compiler.
Efficient
Unlock your system’s full potential. Without sacrificing accuracy, it provide definite performance benefits, including up to 4x reduced memory consumption and 1.5x lower latency.
EASY
Deployment is as simple as a Python call with us. All of the necessary tools are included in to SDK. Unfold them if you’d want, or let us handle the magic from quantization to debugging.
How RooflineAI works
Roofline AI showed how their compiler converts machine learning models from well-known frameworks like PyTorch and TensorFlow into SPIR-V code, a specific language for carrying out parallel computation operations, during the presentation.
As a consequence, developers may more easily get optimal performance without requiring unique setups for every kind of hardware with to a simplified procedure that permits quick, optimized AI model deployment across several platforms.
OneAPI’s ability to enable next-generation AI is demonstrated by Roofline AI’s dedication to improving compiler technology. Roofline AI is not only enhancing AI deployment but also establishing a new benchmark for AI scalability and efficiency with to its unified support for many devices and seamless connectivity with the UXL ecosystem.
Roofline AI is establishing itself as a major force in the development of scalable, high-performance AI applications by pushing the limits of AI compiler technology.
The Contribution of Roofline AI to the Development of Compiler Technology with oneAPI
The oneAPI DevSummit is an event centered around the oneAPI specification, an open programming paradigm that spans industries and was created by Intel to accommodate a variety of hardware architectures.
The DevSummit series, which are held all around the world and are frequently organized by groups like the UXL Foundation, bring together developers, researchers, and business executives to discuss the real-world uses of oneAPI in fields including artificial intelligence (AI), high-performance computing (HPC), edge computing, and more.
Roofline AI took center stage at the recent oneAPI DevSummit, which was organized by the UXL Foundation and Intel Liftoff member, to showcase its creative strategy for improving AI and high-performance HPC performance.
Through RooflineAI’s integration with the UXL framework, they were able to fulfill a key demand in the AI and HPC ecosystem: effective and flexible AI compiler support that can blend in with a variety of devices.
In order to connect AI models and the hardware that runs them, AI compilers are essential. The team from Roofline AI stressed in their pitch that they have developed a strong compiler that facilitates end-to-end model execution for the UXL ecosystem by utilizing the open-source Multi-Level Intermediate Representation (MLIR). With this architecture, developers can map and run AI models on many devices with unmatched flexibility and efficiency.
It’s a clear advancement in device-agnostic AI processing, especially for sectors with a range of hardware requirements. A lightweight runtime based on the Level Zero API, which makes kernel calls and efficiently manages memory, is the foundation of their approach.
In addition to optimizing performance, Roofline AI‘s runtime guarantees compatibility with a variety of Level Zero-compatible hardware, such as Intel GPUs. Because of this interoperability, developers may use their software to control devices outside of the box, reducing the requirement for configuration and increasing the range of hardware alternatives.
