# CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android

arXiv: https://arxiv.org/abs/1511.07376 · HTML: https://mlsanity.com/paper/1511.07376/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2015.

## Built on by (most cited first, 4 of 4)

- [OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload](https://mlsanity.com/paper/2307.03290/) (2023)
- [OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices](https://mlsanity.com/paper/2106.04723/) (2021)
- [SmartSplit: Latency-Energy-Memory Optimisation for CNN Splitting on Smartphone Environment](https://mlsanity.com/paper/2111.01077/) (2021)
- [Towards Implementing Energy-aware Data-driven Intelligence for Smart Health Applications on Mobile Platforms](https://mlsanity.com/paper/2302.00514/) (2023)

## Abstract (arXiv)

Many mobile applications running on smartphones and wearable devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However, performance and energy consumption limitations make the execution of such computationally intensive algorithms on mobile devices prohibitive. We present a GPU-accelerated library, dubbed CNNdroid, for execution of trained deep CNNs on Android-based mobile devices. Empirical evaluations show that CNNdroid achieves up to 60X speedup and 130X energy saving on current mobile devices. The CNNdroid open source library is available for download at https://github.com/ENCP/CNNdroid