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{ "item_title" : "Compiler Engineering for AI Hardware", "item_author" : [" Chatvariety Team "], "item_description" : "Build Compilers for the AI Hardware FrontierThe 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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/819/987/9798199875622_b.jpg", "price_data" : { "retail_price" : "9.99", "online_price" : "9.99", "our_price" : "9.99", "club_price" : "9.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Compiler Engineering for AI Hardware|Chatvariety Team

Compiler Engineering for AI Hardware : MLIR, TVM, XLA, and Custom Backends for Neural Network Accelerators

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Overview

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.

What You Will Master
  • 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.

This item is Non-Returnable

Details

  • ISBN-13: 9798199875622
  • ISBN-10: 9798199875622
  • Publisher: Independently Published
  • Publish Date: June 2026
  • Dimensions: 9 x 6 x 0.19 inches
  • Shipping Weight: 0.3 pounds
  • Page Count: 92

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