Streaming reconstruction of Free-Viewpoint Videos (FVVs) supports immersive Internet of Things (IoT) services, such as telepresence and digital twin visualization. Existing methods suffer from high per-frame optimization time and large storage footprints, limiting deployment on resource-constrained Edge-IoT devices. To address these challenges, we propose Structured Sparse Gaussian Streaming (S$^2$GS), an FVV reconstruction framework that exploits structure-aware temporal sparsity to selectively update Gaussian residuals, enabling efficient streaming without compromising visual fidelity. In the spatial domain, a streaming octree hierarchically organizes Gaussian residuals, capturing spatial correlations that guide residual updates. In the temporal domain, a structured gating mechanism, comprising hierarchical feature propagation (HFP) and Gumbel-Sigmoid sampling, converts hierarchical dynamic cues into sparse residual update decisions under differentiable optimization. A multi-level discrete scheme is further adopted to provide fine-grained control over residual updates while preserving intricate dynamic details. Extensive experiments across consumer GPUs, industrial edge IoT devices, and a physical telepresence testbed demonstrate that S$^2$GS consistently reduces per-frame optimization time and storage footprint while maintaining competitive visual quality. Compared with QUEEN, S$^2$GS reduces per-frame optimization time by 59% and storage costs by 85% on an RTX 4090 GPU. On the Jetson AGX Orin, S$^2$GS delivers the highest rendering throughput (60+ FPS) and the lowest energy consumption among the evaluated methods, demonstrating its potential for deployment in resource-constrained systems.
Yiwei Li, Jiannong Cao, Weixun Gao et al.· 0 citations
Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summaries or other LLM-generated representations. Repeated generation adds cost and can discard answer-bearing details before the system knows what a future query will require. We propose EdgeMem, an agent-memory method built around a simple principle: preserve original interaction turns and organize them through complementary content, temporal, and episodic cues. EdgeMem realizes this principle with a multi-anchor hypergraph constructed by lightweight local processing. Retrieval directly returns source evidence and reserves LLM use for final answer generation, combining structured access to multi-session histories with faithful retention of the original conversation. Experiments on LoCoMo and LongMemEval-S show strong retrieval and memory-grounded question answering; on LoCoMo, EdgeMem achieves the highest strict-judge score among seven reproduced systems under a shared prompt (61.01 versus 58.70), while construction and retrieval require no generative-LLM calls. Overall, EdgeMem shows that preserving and organizing source evidence provides an effective and efficient foundation for agent memory without generative memory management.
Ze-Yang Cui, Jian-Nong Cao, Zhiyuan Wen et al.· 0 citations
Edge AI is evolving from isolated inference toward long-running services that coordinate model pipelines, data streams, state, and accelerators near users and physical environments. Cloud-native and edge-native platforms offer useful foundations, but their primary control objects–containers, nodes, links, and enrolled sites–generally do not expose the model, data, state, quality, and participation semantics required by these services. This paper presents eAI+, a vision for an edgeAI-native service platform built around three first-class control objects: AI service graphs, dynamic edge resource fabrics, and participant contracts. eAI+ aims to preserve service quality under latency, privacy, reliability, cost, and participation constraints through three coordinated mechanisms. Runtime would select safe execution adaptations based on current workload, environment, and contract signals. Deployment would map service-graph components and prepared fallbacks to heterogeneous resources. PolyLink is the participant-contract module for plug-and-play resource onboarding; it would register contributors, verified resource offers, capabilities, and participation terms. Once a resource is onboarded, it would become available to Deployment for placing eligible service-graph components under the registered contract, while PolyLink would maintain metering, reputation, rewards, and exit events. Migration would transfer only continuity-critical state or control when mobility, overload, policy changes, or contributor lifecycle events invalidate the current placement. This framing treats edge AI as a coordinated service-platform problem across models, data, state, resources, and contracts.
Jiannong Cao, Zhiyuan Hu, Mingjin Zhang et al.· International Symposium on S...· 0 citations
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