Demand Layering for Real-Time DNN Inference with Minimized Memory Usage

• URL: http://arxiv.org/abs/2210.04024v1
• Date: Sat, 8 Oct 2022 13:38:48 GMT
• Title: Demand Layering for Real-Time DNN Inference with Minimized Memory Usage
• Authors: Mingoo Ji, Saehanseul Yi, Changjin Koo, Sol Ahn, Dongjoo Seo, Nikil Dutt, Jong-Chan Kim
• Abstract summary: Deep neural network (DNN) model parameters are loaded into GPU memory before execution. We present Demand Layering, which exploits the layer-by-layer execution of DNNs. Our implementation shows a 96.5% memory reduction with just 14.8% delay overhead on average.
• Score: 2.5768647103950357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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