We are excited to announce Intermediate Representation Execution Environment (IREE), the latest sandbox project at the LF AI & Data Foundation. IREE is a versatile and adaptable suite of open source tools designed to improve the deployment of ML model representations across various platforms.
Mission & Vision
IREE’s mission is to create a toolkit that simplifies the deployment of ML programs across various architectures and power regimes, from embedded systems to data centers. Driven by a commitment to versatility and top-tier performance, the project aims to address both current and emerging needs in the ML deployment landscape.
The goals of IREE are tailored to address several critical challenges and opportunities in the ML community:
- Universal Access
IREE strives to unlock access to a vast hardware ecosystem, enabling seamless interaction between any framework and hardware.
- Optimized Performance
Aiming for industry-leading performance, IREE seeks to reduce the total cost of ownership (TCO) for users and developers.
- Standardization
IREE aspires to become the de facto toolkit for heterogeneous computing, establishing a durable compiler platform analogous to historical milestones like the IBM PC in computing.
- Community Collaboration
At its core, IREE is about building an enduring and evolving platform that encourages the ML community to extend and enhance its capabilities.
- Accelerating Innovation
By increasing the speed of ML innovation, IREE intends to empower all practitioners in the field, pushing the boundaries of what’s possible.
As IREE begins its journey under the LF AI & Data Foundation, we invite developers, researchers, and enthusiasts from the ML community to join us. Whether you want to contribute to the project, integrate IREE into your systems, or simply stay informed on the latest advancements, there’s a place for you in the IREE community. Visit the IREE GitHub to learn more.
By supporting a variety of frameworks and targeting heterogeneous platforms, IREE is set to create exciting new options for deploying ML workloads in a variety of contexts.
