MANE is a distributed inference framework that uses a multi‑path tail architecture to allow dynamic accuracy–throughput trade‑offs during edge onloading of deep neural networks. It introduces a novel multi‑path model, a three‑stage training scheme with Joint Head Network Distillation loss, and a hysteresis‑based scheduler with an equitable device‑fallback policy. The system achieves over 80% SLO satisfaction and 6pp higher accuracy than on‑device alternatives while supporting up to 40 concurrent devices.
By Sokratis Nikolaidis, Stylianos I. Venieris, Leonidas Malachias, Iakovos S. Venieris
arXiv:2609.10018v1 Announce Type: new
Abstract: EdgeAI systems are increasingly employing computer vision applications to enable intelligent, on-device decision-making in real-time. However, these de...
By Sudaksh Kalra, Dolly Sapra
arXiv:2606. 29518v1 Announce Type: cross Abstract: With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge.
By Yihan Wang, Huiru Yan, Luxin Zhang, Long Cheng, Weiwei Chen, Ying Wang, Lei Zhang, Cheng Liu, Huawei Li
arXiv:2607. 20490v1 Announce Type: new Abstract: Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralized cloud data centers to the network edge, thereby reducing latency and bandwidth consumption.
By Eug\^enio Santos, Daniel Maia, Stefano Loss, Jos\'e Manoel Silva, Aluizio Rocha Neto, Thais Batista, Everton Cavalcante, N\'elio Cacho, Eduardo Nogueira, Daniel Ara\'ujo, Frederico Lopes
We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments. The framework orchestrates the architecture search process on a server-side NAS service, enabling edge services to derive personalised architectures under device-level energy, computation, and memory constraints.
arXiv:2607. 22805v1 Announce Type: cross Abstract: We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments.
By Keya Patel, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Aneesh Krishna