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User-associated contents play an increasingly important role in modern network applications. With growing deployments of edge servers, the capacity of content storage in edge clusters significantly increases, which provides great potential to satisfy content requests with much shorter latency. However, the large number of contents also causes the difficulty of searching contents on edge servers in different locations because indexing contents costs huge DRAM on each edge server. In this work, we explore the opportunity of efficiently indexing user-associated contents and propose a scalable content-sharing mechanism for edge servers, called EdgeCut, that significantly reduces content access latency by allowing many edge servers to share their cached contents. We design a compact and dynamic data structure called Ludo Locator that returns the IP address of the edge server that stores the requested user-associated content. We have implemented a prototype of EdgeCut in a real network environment running in a public geo-distributed cloud. The experiment results show that EdgeCut reduces content access latency by up to 50% and reduces cloud traffic by up to 50% compared to existing solutions. The memory cost is less than 50MB for 10 million mobile users. The simulations using real network latency data show EdgeCut’s advantages over existing solutions on a large scale.
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This content will become publicly available on November 7, 2026
Tiny Models, Tough Limits: EdgeTinyBench for Border-Control AI on Ten-Watt Platforms
Emerging applications—disaster response drones, in-vehicle assistants, and field medical devices—require on-device language intelligence when cloud links are unreliable, privacy is mandatory, and subsecond latency is nonnegotiable. We benchmark seven SLMs (DistilBERT, MobileBERT, ALBERT, MiniLM, Phi-3 Mini, MobileLLaMA and TinyLLaMA) across four mission-aligned use cases (Watchlist Screening, Threat Detection, Document Triage, Multilingual Routing) on five border-relevant datasets (e.g., GTD, FLORES-200). Under controlled edge-like constraints (mobile-class CPU, 1–8 GB shared memory, intermittent networking), we report task quality (accuracy/F1 or ROUGE), batch-1 inference latency, and peak memory, and we introduce a reproducible, edge-budgeted evaluation protocol for security-critical scenarios. We also outline a path to multimodal edge workloads by pairing compact audio/vision encoders with SLM back ends.
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- Award ID(s):
- 2244283
- PAR ID:
- 10653860
- Publisher / Repository:
- Springer Verlag
- Date Published:
- Subject(s) / Keyword(s):
- Small Language Models · Edge AI · On-Device Inference ·Quantization · Low-Latency NLP · Security-Critical NLP
- Format(s):
- Medium: X
- Location:
- Shanghai, China
- Sponsoring Org:
- National Science Foundation
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