Accelerating GenAI Workloads by Enabling RISC-V Microkernel Support in IREE
Abstract
RISC-V microkernel support is implemented in IREE through MLIR dialect lowering and optimized kernel development, demonstrating performance improvements over existing frameworks.
This project enables RISC-V microkernel support in IREE, an MLIR-based machine learning compiler and runtime. The approach begins by enabling the lowering of MLIR linalg dialect contraction ops to linalg.mmt4d op for the RISC-V64 target within the IREE pass pipeline, followed by the development of optimized microkernels for RISC-V. The performance gains are compared with upstream IREE and Llama.cpp for the Llama-3.2-1B-Instruct model.
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