Validation of GPU Computation in Decentralized, Trustless Networks (2501.05374v1)

Abstract: Verifying computational processes in decentralized networks poses a fundamental challenge, particularly for Graphics Processing Unit (GPU) computations. Our investigation reveals significant limitations in existing approaches: exact recomputation fails due to computational non-determinism across GPU nodes, Trusted Execution Environments (TEEs) require specialized hardware, and Fully Homomorphic Encryption (FHE) faces prohibitive computational costs. To address these challenges, we explore three verification methodologies adapted from adjacent technical domains: model fingerprinting techniques, semantic similarity analysis, and GPU profiling. Through systematic exploration of these approaches, we develop novel probabilistic verification frameworks, including a binary reference model with trusted node verification and a ternary consensus framework that eliminates trust requirements. These methodologies establish a foundation for ensuring computational integrity across untrusted networks while addressing the inherent challenges of non-deterministic execution in GPU-accelerated workloads.