Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network
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doi.org/10.1109/isca.2018.00069 →Countries where authors are citing Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network
This map shows the geographic impact of Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network. 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 Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network more than expected).
Fields of papers citing Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network
This network shows the impact of Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network.
About Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network
This paper, published in 2018, received 395 indexed citations . Written by Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Joon Kyung Kim, Vikas Chandra and Hadi Esmaeilzadeh covering the research area of Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (248 citations), Electrical and Electronic Engineering (198 citations), Artificial Intelligence (192 citations), Hardware and Architecture (74 citations) and Computer Networks and Communications (40 citations).
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/isca.2018.00069.