Compiler Engineering for AI Hardware: MLIR, TVM, XLA, and Custom Backends for Neural Network Accelerators (AI Infrastructure, Hardware & Compiler Engineering Series) - Tapa blanda

Sinopsis

Build Compilers for the AI Hardware Frontier

The explosion of custom AI accelerators—including the Apple Neural Engine, Google TPU, AWS Inferentia, and Qualcomm Hexagon—has created an urgent demand for compiler engineers. These specialists must understand the entire software stack, from neural network graph representation down to hardware-specific code generation. Compiler Engineering for AI Hardware provides the definitive technical foundation for designing, building, and optimizing modern AI compilation pipelines.

This hands-on guide bridges the critical gap between high-level machine learning frameworks and low-level hardware design. You will explore real-world compiler architectures and learn how to translate deep learning models into highly efficient machine instructions.

• MLIR Architecture: Master multi-level IR design, custom dialect creation, and progressive lowering strategies to LLVM IR.
• TVM and Relay/Relax: Leverage TVM, Relax, and MetaSchedule for graph-level optimizations, operator fusion, and auto-tuning.
• XLA and PJRT: Understand Google's compiler pipeline, HLO representations, fusion strategies, and hardware runtimes.
• Custom Backends: Build custom MLIR dialects and target-specific code generation passes for novel hardware targets.
• Memory and Layout Optimizations: Implement memory planning algorithms, loop transformations, and data layout changes to maximize throughput.

Whether you are a hardware architect designing next-generation silicon or a software engineer optimizing deep learning inference, this book delivers the practical code examples, IR listings, and architectural insights needed to build production-grade compiler pipelines. Step into the future of systems engineering and master the AI compiler stack today.

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