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RL4ReAl: Reinforcement Learning for Register Allocation

04/05/2022

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by S. VenkataKeerthy, et al.

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We propose a novel solution for the Register Allocation problem, leveraging multi-agent hierarchical Reinforcement Learning. We formalize the constraints that precisely define the problem for a given instruction-set architecture, while ensuring that the generated code preserves semantic correctness. We also develop a gRPC based framework providing a modular and efficient compiler interface for training and inference. Experimental results match or outperform the LLVM register allocators, targeting Intel x86 and ARM AArch64.

S. VenkataKeerthy
2 publications
Siddharth Jain
6 publications
Rohit Aggarwal
2 publications
Albert Cohen
19 publications
Ramakrishna Upadrasta
8 publications

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