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@inproceedings{de_prado_learning_2019, title = {Learning to infer: {RL}-based search for {DNN} primitive selection on Heterogeneous Embedded Systems}, url = {https://arxiv.org/abs/1811.07315v1}, doi = {https://doi.org/10.23919/DATE.2019.8714959}, shorttitle = {Learning to infer}, abstract = {Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices. While Convolutional Neural Networks' accuracy has achieved a mature and remarkable state, inference latency and throughput are a major concern especially when targeting low-cost and low-power embedded platforms. {CNNs}' inference latency may become a bottleneck for Deep Learning adoption by industry, as it is a crucial specification for many real-time processes. Furthermore, deployment of {CNNs} across heterogeneous platforms presents major compatibility issues due to vendor-specific technology and acceleration libraries. In this work, we present {QS}-{DNN}, a fully automatic search based on Reinforcement Learning which, combined with an inference engine optimizer, efficiently explores through the design space and empirically finds the optimal combinations of libraries and primitives to speed up the inference of {CNNs} on heterogeneous embedded devices. We show that, an optimized combination can achieve 45x speedup in inference latency on {CPU} compared to a dependency-free baseline and 2x on average on {GPGPU} compared to the best vendor library. Further, we demonstrate that, the quality of results and time "to-solution" is much better than with Random Search and achieves up to 15x better results for a short-time search.}, eventtitle = {{DATE} 2019}, booktitle = {Proceedings of Design, Automation and Test in Europe Conference, {DATE} 19. March 2019}, author = {de Prado, Miguel and Pazos, Nuria and Benini, Luca}, urldate = {2019-01-29}, date = {2019}, langid = {english}, }