LipCache: A Local Inference Proxy with Certified Caching for Edge Image Classification Service

• URL: http://arxiv.org/abs/2608.13144v1
• Date: Thu, 13 Aug 2026 12:16:58 GMT
• Title: LipCache: A Local Inference Proxy with Certified Caching for Edge Image Classification Service
• Abstract summary: textttLipCache is a certified semantic caching framework for image classification.<n>textttGuardNet maps inputs into a low-dimensional feature space subject to a Lipschitz constraint.<n>textttLipCache achieves a measured speedup of up to $1.65times$ with limited end-to-end accuracy degradation.
• Score: 8.191333850528093
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As edge-side vision services continue to expand toward low-latency, high-throughput scenarios, reducing the inference cost of vision models without sacrificing reliability has become a central concern. Existing semantic caching methods largely rely on empirical similarity thresholds; while such thresholds improve hit rates, they tend to introduce silent misclassifications near decision boundaries. To address this, we propose \texttt{LipCache}, a certified semantic caching framework for image classification. Without modifying the existing deployed main model, \texttt{MainNet}, the framework introduces a lightweight network, \texttt{GuardNet}, that maps inputs into a low-dimensional feature space subject to a Lipschitz constraint. It then computes a per-sample certified reuse radius from the local classification margin and the spectral norm of the classification head. At runtime, a cached result is reused only when the query feature falls inside the certified reuse ball; otherwise, the query falls back to \texttt{MainNet}. Thus, cache hits are transformed from empirical threshold tests into geometric certification decisions with explicit theoretical boundaries. Across standard image classification tasks like CIFAR, Tiny-ImageNet, and SVHN, \texttt{LipCache} achieves a measured speedup of up to $1.65\times$ with limited end-to-end accuracy degradation, while all accepted cache hits satisfy the \texttt{GuardNet}-side certified-consistency condition. Furthermore, an enhanced \texttt{GuardNet} training recipe substantially improves cache hit rates in the Tiny-ImageNet multi-class extension while maintaining a certified-consistency rate of $100\%$. These results demonstrate that per-sample certified reuse can reduce main-model fallback while preserving theoretical consistency, providing a feasible approach to reliable cache-assisted inference at the edge.

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