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
The increasing reliance on decentralized machine learning systems and the inherent limitations of blockchain for resource-intensive tasks necessitate verifiable computing solutions now.
This research addresses a critical trust deficit in outsourced AI computations, enabling secure and reliable offloading of complex AI tasks to untrusted parties, which is vital for scalable decentralized AI.
The ability to verifiably offload deep neural network inference without re-execution mitigates the computational burden on blockchain and decentralised AI systems, fostering greater adoption and capability.
- · Decentralized AI platforms
- · Blockchain protocols
- · Cloud computing providers
- · AI-as-a-Service companies
- · Centralized AI inference providers (if not adaptable)
- · Non-verifiable computing solutions
- · Small blockchain networks lacking computational capacity
More widespread and trustless adoption of decentralized AI and machine learning applications becomes possible.
Reduced operational costs and increased efficiency for AI service providers by offloading computation without sacrificing integrity.
New business models emerge around verifiable AI computing and AI agents operating in trust-minimized environments.
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Read at arXiv cs.LG