Compiler Engineering for AI Hardware: MLIR, TVM, XLA, and Custom Backends for Neural Network Accelerators
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.
What You Will MasterMLIR 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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