AMD MLIR-AIE Releases New AIECC C++ Compiler To Help Bring New Workloads To Ryzen AI NPUs
AMD MLIR-AIE Releases New AIECC C++ Compiler To Help Bring New Workloads To Ryzen AI NPUs
https://www.phoronix.com/news/MLIR-AIE-1.3
Publish Date: 2026-03-17 15:00:00
Source Domain: www.phoronix.com
AMD Ryzen AI NPUs are now running LLMs on Linux with the recently debuted Lemonade 10.0 server and FastFlowLM 0.9.35 adding Linux support. In addition to those software components, AMD engineers have also been developing MLIR-AIE as a compiler toolchain for AMD AI Engine devices such as Ryzen AI NPUs in leveraging LLVM-based code generation with the Multi-Level Intermediate Representation (MLIR). Out today is MLIR-AIE v1.3 with some notable new features.
MLIR-AIE provides Python APIs and more for leveraging AMD NPUs as an alternative to the traditional Ryzen AI software workflows focused on AI inferencing. The hope has been with MLIR-AIE to also open up the Ryzen AI NPUs to digital signal processing and other non-AI/ML workloads thanks to the versatility of LLVM and MLIR. MLIR-AIE also ties into AMD’s Peano code. Beyond Ryzen AI NPUs, MLIR-AIE also benefits the other AMD-Xilinx accelerator products.
Thanks to the broad support for MLIR in the software ecosystem and even across hardware vendors, going the MLIR route allows versatility too for targeting other AMD wares such as Radeon/Instinct products via ROCm, CPUs via LLVM, etc.
New with MLIR-AIE 1.3 is a new C++ aiecc compiler to serve as an alternative to the project’s existing Python-based tooling. Code comments in the new AIECC C++ file sum up this new compiler as:
“This is the main entry point for the AIE compiler driver (aiecc). It orchestrates the compilation flow for AIE devices.
This C++ implementation provides similar functionality to the Python aiecc.py tool with the following architecture:
1. Command-line argument parsing using LLVM CommandLine library
2. MLIR module loading and parsing
3. MLIR transformation pipeline execution
4. Core compilation (xchesscc/peano)
5. NPU instruction generation
6. CDO/PDI/xclbin generation
7. Multi-device support”
The C++ aiecc compiler delivers better performance, supports C++17, can handle MLIR module loading and parsing, NPU instruction generation, ELF…
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