Accelerator Build Infrastructure
Creating tools and systems for developing, testing, and validating AI accelerator hardware and software stacks.
Overview
Accelerator build infrastructure encompasses the tools, systems, and processes for developing new AI accelerators from concept to production. This includes simulation frameworks for testing designs before fabrication, compiler toolchains for targeting new hardware, validation suites for ensuring correctness, and continuous integration systems for hardware and software co-development. Building accelerators requires massive engineering effort, and good infrastructure multiplies the productivity of development teams.
Key Research Areas
Hardware simulation and emulation
Compiler toolchain development
Validation and correctness testing
Performance benchmarking frameworks
Hardware-software co-design tools
Continuous integration for hardware
Research Challenges
Long iteration cycles for hardware changes
Complexity of modern accelerator designs
Validating correctness across all cases
Simulating performance before fabrication
Coordinating hardware and software teams
Testing at scale before production
Practical Applications
Developing next-generation AI chips
Validating accelerator designs pre-tapeout
Building compiler backends for new hardware
Performance evaluation of design choices
Regression testing for hardware changes
Benchmarking competitor accelerators
Technical Deep Dive
Accelerator build infrastructure includes multiple layers. Hardware simulation uses tools like Verilator or commercial simulators to test RTL designs. Emulation on FPGAs provides faster validation for complex designs. Compiler infrastructure includes LLVM backends or custom toolchains that lower high-level operations to hardware instructions. Validation frameworks test correctness using formal verification methods and extensive test suites. Performance models predict behavior of designs before expensive fabrication. CI/CD systems run nightly builds and tests, catching regressions early. Debugging tools help engineers understand hardware behavior at various abstraction levels.
Future Research Directions
Future infrastructure will increasingly use ML to optimize accelerator designs and compilation. Cloud-based hardware simulation may reduce local compute requirements. Better hardware-software co-simulation enables optimization across the full stack. Automated generation of compiler backends from hardware specifications could accelerate support for new designs. As accelerator designs become more complex and diverse, good infrastructure becomes even more critical for managing complexity and maintaining development velocity.
Related Research Topics
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