FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
arXiv:2605.22868v1 Announce Type: new Abstract: Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, latency, and reliability budgets demand run-time adaptivity. In practice, deciding what to compute and transmit at each point is pivotal; yet as multimodal sensor suites (cameras, LiDAR/depth, etc.) proliferate at the edge, most prior approaches either (i) fuse modalities on powerful servers or (ii) apply uni-modal near-sensor filters that ignore cross-modal dependencies, leading to redundant transmis
The proliferation of multimodal sensor suites and the demand for real-time adaptivity in autonomous systems necessitate more efficient edge intelligence solutions.
This research addresses critical constraints like energy, latency, and reliability in edge computing, which are vital for the scalability and performance of AI in autonomous and industrial applications.
The approach shifts towards more sophisticated near-sensor processing, enabling runtime-adaptive multimodal fusion directly at the edge rather than relying solely on cloud or powerful servers.
- · Edge AI hardware developers
- · Autonomous systems manufacturers
- · Smart industry deployments
- · AI research institutions
- · Cloud-centric AI model developers
- · Legacy uni-modal sensor manufacturers
Improved efficiency and reduced data transmission requirements for edge AI applications.
Accelerated deployment of more capable and reliable autonomous systems and smart industrial solutions.
Increased decentralization of computational power, potentially reducing reliance on centralized cloud infrastructure for certain AI tasks.
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Read at arXiv cs.LG