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.
