Energy-Quality Scalable Monocular Depth Estimation on Low-Power CPUs
| Autor: | Valentino Peluso, Fabio Tosi, Stefano Mattoccia, Antonio Cipolletta, Andrea Calimera, Filippo Aleotti, Matteo Poggi |
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| Přispěvatelé: | Cipolletta, Antonio, Peluso, Valentino, Calimera, Andrea, Poggi, Matteo, Tosi, Fabio, Aleotti, Filippo, Mattoccia, Stefano |
| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Deep learning Process (computing) deep learning monocular depth estimation Program optimization Convolutional neural networks (CNNs) Convolutional neural network Computer Science Applications Computer engineering Hardware and Architecture Signal Processing Scalability Memory footprint Monocular Depth Estimation Energy-Quality Scaling Embedded Systems Low-Power CPUs Convolutional Neural Networks Deep Learning embedded systems Artificial intelligence energy-quality scaling low-power CPUs business Quantization (image processing) Information Systems Efficient energy use |
| Zdroj: | IEEE Internet of Things Journal. 9:25-36 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2021.3080827 |
| Popis: | The recent advancements in deep learning have demonstrated that inferring high-quality depth maps from a single image has become feasible and accurate, thanks to convolutional neural networks (CNNs), but how to process such compute- and memory-intensive models on portable and low-power devices remains a concern. Dynamic energy-quality scaling is an interesting yet less explored option in this field. It can improve efficiency through opportunistic computing policies where performances are boosted only when needed, achieving on average substantial energy savings. Implementing such a computing paradigm encompasses the availability of a scalable inference model, which is the target of this work. Specifically, we describe and characterize the design of an energy-quality scalable pyramidal network (EQPyD-Net), a lightweight CNN capable of modulating at runtime the computational effort with minimal memory resources. We describe the architecture of the network and the optimization flow, covering the important aspects that enable the dynamic scaling, namely, the optimized training procedures, the compression stage via fixed-point quantization, and the code optimization for the deployment on commercial low-power CPUs adopted in the edge segment. To assess the effect of the proposed design knobs, we evaluated the prediction quality on the standard KITTI data set and the energy and memory resources on the ARM Cortex-A53 CPU. The collected results demonstrate the flexibility of the proposed network and its energy efficiency. EQPyD-Net can be shifted across five operating points, ranging from a maximum accuracy of 82.2% with 0.4 Frame/J and up to 92.6% of energy savings with 6.1% of accuracy loss, still keeping a compact memory footprint of 5.2 MB for the weights and 38.3 MB (in the worst case) for the processing. |
| Databáze: | OpenAIRE |
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