2017/04/12 by Dawei Sun, Shaoshan Liu, Sun, Dawei +4 · 2 citations
Computer Science · #Distributed and Parallel Computing Systems #Embedded Systems Design Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques #cs.LG
paper · pdf · doi:10.48550/arxiv.1704.03751
4 pages, 4 figures
openalex publication_date 2017/04/12 · arxiv created 2017/04/14 · arxiv updated 2017/04/17 · openalex created_date 2017/04/28 · openalex updated_date 2026/07/28
When you need to enable deep learning on low-cost embedded SoCs, is it better to port an existing deep learning framework or should you build one from scratch? In this paper, we share our practical experiences of building an embedded inference engine using ARM Compute Library (ACL). The results show that, contradictory to conventional wisdoms, for simple models, it takes much less development time to build an inference engine from scratch compared to porting existing frameworks. In addition, by utilizing ACL, we managed to build an inference engine that outperforms TensorFlow by 25%. Our conclusion is that, on embedded devices, we most likely will use very simple deep learning models for inference, and with well-developed building blocks such as ACL, it may be better in both performance and development time to build the engine from scratch.