Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
MLIR in Action: A Practical Guide to Scalable Model Optimization and Hardware Acceleration With OpenXLA, IREE and Mojo

MLIR in Action: A Practical Guide to Scalable Model Optimization and Hardware Acceleration With OpenXLA, IREE and Mojo

Orian, Reid

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798272080059
Publisher: Independently Published
Published: Oct 29 2025
Pages: 246
Weight: 1.28
Height: 0.52 Width: 8.50 Depth: 11.00
Language: English
MLIR in Action: A Practical Guide to Scalable Model Optimization and Hardware Acceleration with OpenXLA, IREE, and Mojo

Unlock the full power of machine learning optimization and next-generation compiler design with MLIR in Action - your complete, hands-on guide to mastering the Multi-Level Intermediate Representation (MLIR) ecosystem.

Built for engineers, researchers, and AI practitioners, this book takes you on a step-by-step journey through the core concepts, workflows, and real-world applications of MLIR - the backbone of modern compiler infrastructures like OpenXLA, IREE, and Mojo. Learn how to optimize, transform, and deploy machine learning models efficiently across CPUs, GPUs, and custom accelerators using a unified and extensible compiler stack.

Inside this practical guide, you will discover how to:

- Understand the architecture and principles of MLIR in depth.

- Build, extend, and debug custom MLIR dialects and passes.

- Integrate MLIR with leading frameworks such as TensorFlow, PyTorch, and Mojo.

- Leverage OpenXLA and IREE for portable, high-performance model deployment.

-Automate builds, CI/CD pipelines, and cloud deployment for scalable production systems.

-Visualize, profile, and debug IR flows for performance tuning and optimization.

- Stay ahead of the curve with insights into emerging compiler standards, AI-driven optimization, and future MLIR trends.

From theory to practice, every chapter blends clear explanations with real code examples, text-based flowcharts, and implementation checklists. Whether you are optimizing large-scale AI workloads or exploring compiler-based acceleration, MLIR in Action gives you the tools to move from concept to production with confidence.

Perfect for:

- Machine Learning Engineers

- Compiler Developers

- AI Infrastructure Architects

- Systems Programmers

- Researchers exploring hardware-aware AI optimization

MLIR in Action bridges the gap between research and real-world deployment - helping you build the scalable, efficient, and future-ready AI systems that power the next wave of machine learning innovation.

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