# SparOA: Sparse and Operator-aware Hybrid Scheduling for Edge DNN Inference

arXiv: https://arxiv.org/abs/2511.19457 · HTML: https://mlsanity.com/paper/2511.19457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2025.

## Abstract (arXiv)

The resource demands of deep neural network (DNN) models introduce significant performance challenges, especially when deployed on resource-constrained edge devices. Existing solutions like model compression often sacrifice accuracy, while specialized hardware remains costly and inflexible. Hybrid inference methods, however, typically overlook how operator characteristics impact performance. In this work, we present SparOA, a CPU-GPU hybrid inference framework, which leverages both sparsity and computational intensity to optimize operator scheduling. SparOA embraces aforementioned challenges through three key components: (1) a threshold predictor that accurately determines optimal sparsity and computational intensity thresholds; (2) a reinforcement learning-based scheduler that dynamically optimizes resource allocation based on real-time hardware states; and (3) a hybrid inference engine that enhances efficiency through asynchronous execution and batch size optimization.Extensive results show that SparOA achieves an average speedup of 1.22-1.31x compared to all baselines, and outperforms the CPU-Only by up to 50.7x. Also, SparOA achieves optimal energy-per-inference, consuming 7%-16% less energy than the SOTA co-execution baseline.