DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
Impact in
Classified as
- Journal
- UCL Discovery (University College London)
In The Last Decade
doi.org/10.1109/ipsn.2016.7460664 →Countries where authors are citing DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
This map shows the geographic impact of DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices more than expected).
Fields of papers citing DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
This network shows the impact of DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices.
About DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
This paper, published in 2016, received 335 indexed citations . Written by Nicholas D. Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro and Fahim Kawsar covering the research area of Computer Networks and Communications and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (211 citations), Computer Networks and Communications (141 citations), Artificial Intelligence (116 citations), Electrical and Electronic Engineering (89 citations) and Hardware and Architecture (32 citations). Published in UCL Discovery (University College London).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/ipsn.2016.7460664.