Roofline AI: What is it?
Roofline AI is a software development kit (SDK) that is used to create Edge AI. Roofline AI GmbH, a spin-off from RWTH Aachen University, created it.
RooflineAI's SDK facilitates the following:
- Flexibility: You may import models from any AI framework, such as ONNX, PyTorch, and TensorFlow.
- Roofline AI performs really well.
- Usability: Using RooflineAI is easy.
- Deploying on a range of hardware, including CPUs, MPUs, MCUs, GPUs, and specialist AI hardware accelerators, is made easy via RooflineAI.
- The retargetable AI compiler technology from RooflineAI encourages cooperation between the open-source community and chip makers.
- The Roofline model is a computer science approach that helps programmers determine the compute-memory ratio of a task. It is used to assess the computational efficiency and memory bandwidth of AI architectures.
Redefining the deployment of edge AI
Edge AI is evolving rapidly. New models that are developing quickly, such as LLMs, make it hard to predict when technology will evolve. At the same time, hardware solutions are become increasingly varied and complex.
Due to their inability to keep up with this rate, conventional deployment methodologies have become major barriers to the adoption of edge AI. They are not very versatile, have poor performance, and are unpleasant to use.
Roofline revolutionizes this process with a software solution that offers unmatched flexibility, exceptional performance, and ease of use. You may import models from any framework and distribute them across several devices with only one Python line.
Advantages
Adaptable
Install any model on a variety of target devices from any framework. The retargetable compiler enables the deployment of innovative programs on the most efficient hardware.
Effective
Unlock the full potential of your system. It offers noticeable speed advantages, such as up to 4x lower memory usage and 1.5x lower latency, without compromising accuracy.
SIMPLE
With us, deployment is as easy as making a Python call. The SDK comes with all the required tools. Let us do the magic, from quantization to debugging, or unfold them if you'd like.
How RooflineAI operates
During the presentation, Roofline AI demonstrated how their compiler transforms machine learning models from popular frameworks such as PyTorch and TensorFlow into SPIR-V code, a specialized language for performing parallel computing operations.
Because of a streamlined process that enables rapid, optimized AI model deployment across several platforms, developers may more quickly achieve maximum performance without needing special settings for every kind of hardware.
Roofline AI's commitment to advancing compiler technology demonstrates how OneAPI may facilitate next-generation AI. Because of its seamless integration with the UXL ecosystem and unified support for several devices, Roofline AI is not only improving AI deployment but also setting a new standard for AI scalability and efficiency.
By pushing the boundaries of AI compiler technology, Roofline AI is becoming a significant player in the creation of scalable, high-performance AI applications.
Roofline AI's Role in the Advancement of Compiler Technology with OneAPI
The oneAPI specification, an open programming paradigm that crosses industries and was developed by Intel to support a range of hardware architectures, is the focus of the oneAPI DevSummit.
Often organized by organizations such as the UXL Foundation, the DevSummit series brings together developers, researchers, and business executives to discuss the practical applications of oneAPI in domains such as edge computing, high-performance computing (HPC), artificial intelligence (AI), and more. These events take place all over the world.
At the recent oneAPI DevSummit, which was hosted by the UXL Foundation and Intel Liftoff member, Roofline AI took center stage to demonstrate its innovative approach to enhancing AI and high-performance HPC performance.
A major need in the AI and HPC ecosystem was met by RooflineAI's integration with the UXL framework: efficient and adaptable AI compiler support that works with a range of devices.
AI compilers are crucial for establishing a connection between AI models and the hardware that executes them. In their pitch, the Roofline AI team emphasized that they have created a powerful compiler that uses the open-source Multi-Level Intermediate Representation (MLIR) to enable end-to-end model execution for the UXL ecosystem. This architecture offers developers unparalleled flexibility and speed in mapping and executing AI models across many devices.
Device-agnostic AI processing has clearly advanced, particularly for industries with varying hardware needs. Their method is built on a lightweight runtime based on the Level Zero API, which effectively manages memory and makes kernel calls.
Roofline AI's runtime ensures compatibility with a range of Level Zero-compatible hardware, including Intel GPUs, while also improving performance. Developers may use their software to control devices outside of the box thanks to this interoperability, which lowers the need for configuration and expands the selection of hardware options.
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