1. Demand Layering for Real-Time DNN Inference with Minimized Memory Usage
- Author
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Ji, Mingoo, Yi, Saehanseul, Koo, Changjin, Ahn, Sol, Seo, Dongjoo, Dutt, Nikil, and Kim, Jong-Chan
- Subjects
Computer Science - Machine Learning - Abstract
When executing a deep neural network (DNN), its model parameters are loaded into GPU memory before execution, incurring a significant GPU memory burden. There are studies that reduce GPU memory usage by exploiting CPU memory as a swap device. However, this approach is not applicable in most embedded systems with integrated GPUs where CPU and GPU share a common memory. In this regard, we present Demand Layering, which employs a fast solid-state drive (SSD) as a co-running partner of a GPU and exploits the layer-by-layer execution of DNNs. In our approach, a DNN is loaded and executed in a layer-by-layer manner, minimizing the memory usage to the order of a single layer. Also, we developed a pipeline architecture that hides most additional delays caused by the interleaved parameter loadings alongside layer executions. Our implementation shows a 96.5% memory reduction with just 14.8% delay overhead on average for representative DNNs. Furthermore, by exploiting the memory-delay tradeoff, near-zero delay overhead (under 1 ms) can be achieved with a slightly increased memory usage (still an 88.4% reduction), showing the great potential of Demand Layering., Comment: 14 pages, 16 figures. Accepted to the 43rd IEEE Real-Time Systems Symposium (RTSS), 2022
- Published
- 2022