Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission

• URL: http://arxiv.org/abs/2601.08135v1
• Date: Tue, 13 Jan 2026 01:56:46 GMT
• Title: Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission
• Authors: Zengzipeng Tang, Yuxuan Sun, Wei Chen, Jianwen Ding, Bo Ai, Yulin Shao,
• Abstract summary: Device-edge collaborative inference with Deep Neural Networks (DNNs) faces fundamental trade-offs among accuracy, latency and energy consumption.<n>This paper proposes a novel ENergy-ACcuracy Hierarchical optimization framework for split Inference, named ENACHI.<n> Experiments on ImageNet dataset demonstrate that ENACHI outperforms state-of-the-art benchmarks under varying deadlines and bandwidths.
• Score: 23.81409473238433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Device-edge collaborative inference with Deep Neural Networks (DNNs) faces fundamental trade-offs among accuracy, latency and energy consumption. Current scheduling exhibits two drawbacks: a granularity mismatch between coarse, task-level decisions and fine-grained, packet-level channel dynamics, and insufficient awareness of per-task complexity. Consequently, scheduling solely at the task level leads to inefficient resource utilization. This paper proposes a novel ENergy-ACcuracy Hierarchical optimization framework for split Inference, named ENACHI, that jointly optimizes task- and packet-level scheduling to maximize accuracy under energy and delay constraints. A two-tier Lyapunov-based framework is developed for ENACHI, with a progressive transmission technique further integrated to enhance adaptivity. At the task level, an outer drift-plus-penalty loop makes online decisions for DNN partitioning and bandwidth allocation, and establishes a reference power budget to manage the long-term energy-accuracy trade-off. At the packet level, an uncertainty-aware progressive transmission mechanism is employed to adaptively manage per-sample task complexity. This is integrated with a nested inner control loop implementing a novel reference-tracking policy, which dynamically adjusts per-slot transmit power to adapt to fluctuating channel conditions. Experiments on ImageNet dataset demonstrate that ENACHI outperforms state-of-the-art benchmarks under varying deadlines and bandwidths, achieving a 43.12\% gain in inference accuracy with a 62.13\% reduction in energy consumption under stringent deadlines, and exhibits high scalability by maintaining stable energy consumption in congested multi-user scenarios.

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