Kernel Contracts is a specification language that formalizes correctness requirements for ML kernels to ensure consistent results across heterogeneous silicon platforms.
Impacts of floating-point non-associativity on reproducibility for hpc and deep learning applications.arXiv preprint
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LLMs show implementation-induced randomness even at T=0 that can be characterized as an effective background temperature T_bg estimated via an ideal reference system.
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Kernel Contracts: A Specification Language for ML Kernel Correctness Across Heterogeneous Silicon
Kernel Contracts is a specification language that formalizes correctness requirements for ML kernels to ensure consistent results across heterogeneous silicon platforms.
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Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models
LLMs show implementation-induced randomness even at T=0 that can be characterized as an effective background temperature T_bg estimated via an ideal reference system.