ESCAN: Efficient GPU sharing for cascade neural network inference
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View abstract on PubMed
Summary
This summary is machine-generated.We developed ESCAN, a GPU-sharing framework for cascade neural networks, to improve inference efficiency. ESCAN optimizes device sharing by balancing resource gains with early-exit mechanisms, enhancing low-latency services.
Area Of Science
- Computer Science
- Artificial Intelligence
- Machine Learning
Background
- Cascading models balance efficiency and accuracy in industrial deployments.
- Low-latency services demand optimized execution efficiency and device utilization.
- Existing GPU sharing (Multiprocessing Services) struggles with cascade models due to early-exit and execution order.
Purpose Of The Study
- To address the challenges of applying GPU sharing to cascade neural networks.
- To propose a framework that optimizes inference efficiency for cascade models.
- To enhance device utilization and reduce latency in cloud-based inference services.
Main Methods
- Analyzed cascade neural network characteristics and device-sharing techniques.
- Developed ESCAN, a GPU-sharing optimization framework for online inference.
- Integrated exit-ratio-aware batch-parallel execution and resource allocation algorithms in PyTorch.
Main Results
- ESCAN improves inference efficiency by an average of 19.53% compared to parallel execution.
- Significantly enhances the efficiency of searching for computation resource allocation schemes.
- Optimizes computational resource utilization through effective GPU-sharing.
Conclusions
- ESCAN provides an effective solution for GPU-sharing optimization in cascade neural networks.
- Achieves a balance between device-sharing gains and early-exit computation wastage.
- Delivers a low-latency, high-precision optimization for interactive online services using cascade models.
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