{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/benchmark-analysis-of-representative-deep","title":"Benchmark Analysis of Representative Deep Neural Network Architectures","arxiv_id":"1810.00736","date":"2018-10-01","proceeding":null,"authors":["Simone Bianco","Remi Cadene","Luigi Celona","Paolo Napoletano"],"abstract":"This work presents an in-depth analysis of the majority of the deep neural\nnetworks (DNNs) proposed in the state of the art for image recognition. For\neach DNN multiple performance indices are observed, such as recognition\naccuracy, model complexity, computational complexity, memory usage, and\ninference time. The behavior of such performance indices and some combinations\nof them are analyzed and discussed. To measure the indices we experiment the\nuse of DNNs on two different computer architectures, a workstation equipped\nwith a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson\nTX1 board. This experimentation allows a direct comparison between DNNs running\non machines with very different computational capacity. This study is useful\nfor researchers to have a complete view of what solutions have been explored so\nfar and in which research directions are worth exploring in the future; and for\npractitioners to select the DNN architecture(s) that better fit the resource\nconstraints of practical deployments and applications. To complete this work,\nall the DNNs, as well as the software used for the analysis, are available\nonline.","url_abs":"http://arxiv.org/abs/1810.00736v2","url_pdf":"http://arxiv.org/pdf/1810.00736v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"benchmark-analysis-of-representative-deep","repo_url":"https://github.com/CeLuigi/models-comparison.pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"benchmark-analysis-of-representative-deep","repo_url":"https://github.com/deyingk/Interesting_Papers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.00736","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00736"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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