aiqbench analysis
TechPulse take: This research introduces Range-Arithmetic, a framework that converts non-arithmetic deep learning operations into verifiable arithmetic steps, eliminating the need for complex Boolean encodings or large lookup tables. By leveraging sum-check protocols and range proofs, the method significantly reduces verification costs, computational load on untrusted parties, and communication overhead compared to existing approaches. The work offers a practical pathway for efficient, verifiable inference in decentralized machine learning systems without sacrificing performance.
\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party
arXiv:2505.17623v2 Announce Type: replace-cross Abstract: Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural network (DNN) inference are offloaded to external participants due to blockchain limitations. This creates a need to verify the correctness of outsourced computations without re-execution. We propose \texttt{Range-Arithmetic}, a novel framework for efficient and verifiable DNN inference that transforms non-arithmetic operations, such as rounding after fixed-point matrix multiplication and ReLU, into arithmetic steps verifiable using sum-check protocols and concatenated range proofs. Our approach avoids the complexity of Boolean encoding, high-degree polynomials, and large lookup tables while remaining compatible with finite-field-based proof systems. Experimental results show that our method not only matches the performance of existing approaches, but also reduces the computational cost of verifying the results, the computational effort required from the untrusted party performing the DNN inference, and the communication overhead between the two sides.
Abstract:Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural network (DNN) inference are offloaded to external participants due to blockchain limitations. This creates a need to verify the correctness of outsourced computations without re-execution. We propose \texttt{Range-Arithmetic}, a novel framework for efficient and verifiable DNN inference that transforms non-arithmetic operations, such as rounding after fixed-point matrix multiplication and ReLU, into arithmetic steps verifiable using sum-check protocols and concatenated range proofs. Our approach avoids the complexity of Boolean encoding, high-degree polynomials, and large lookup tables while remaining compatible with finite-field-based proof systems. Experimental results show that our method not only matches the performance of existing approaches, but also reduces the computational cost of verifying the results, the computational effort required from the untrusted party performing the DNN inference, and the communication overhead between the two sides.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Performance (cs.PF)
arXiv:2505.17623 [cs.CR]
(or arXiv:2505.17623v2 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2505.17623 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 247 words