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Optimising Convolutional Neural Networks Inference on Low-Powered GPUs
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rovder, Simon Cano, José O'Boyle, Michael |
| Copyright Year | 2019 |
| Abstract | In this paper we present effective optimisation techniques for accelerating convolutional neural networks inference on low-powered heterogeneous devices with OpenCL. Using LeNet and VGG-16 as test networks, we implement a custom neural network system in OpenCL and optimise it to minimise their inference times. Our baseline system shows a speedup of 17x for LeNet. We also outline two methods for fast convolution: an iterative vectorised approach and a Morton GEMM based approach. The two approaches demonstrate VGG-16 inference speeds up to 3x faster than current state-of-the-art systems and outperform other custom neural network systems by speedup factors of up to 1.82x. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.dcs.gla.ac.uk/~josecr/pub/2019_multiprog.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
