{"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/the-challenge-of-multi-operand-adders-in-cnns","title":"The Challenge of Multi-Operand Adders in CNNs on FPGAs: How not to solve it!","arxiv_id":"1807.00217","date":"2018-06-30","proceeding":null,"authors":["Kamel Abdelouahab","François Berry","Maxime Pelcat"],"abstract":"Convolutional Neural Networks (CNNs) are computationally intensive algorithms that currently require dedicated hardware to be executed. In the case of FPGA-Based accelerators, we point-out in this work the challenge of Multi-Operand Adders (MOAs) and their high resource utilization in an FPGA implementation of a CNN. To address this challenge, two optimization strategies, that rely on time-multiplexing and approximate computing, are investigated. At first glance, the two strategies looked promising to reduce the footprint of a given architectural mapping, but when synthesized on the device, none of them gave the expected results. Experimental sections analyze the reasons of these unexpected results.","url_abs":"http://arxiv.org/abs/1807.00217v1","url_pdf":"http://arxiv.org/pdf/1807.00217v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"the-challenge-of-multi-operand-adders-in-cnns","repo_url":"https://github.com/KamelAbdelouahab/Multi-Operand-Adder","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-challenge-of-multi-operand-adders-in-cnns","repo_url":"https://github.com/2023-MindSpore-1/ms-code-224/tree/main/AdderQuant","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"the-challenge-of-multi-operand-adders-in-cnns","repo_url":"https://github.com/2024-MindSpore-1/Code5/tree/main/AdderQuant","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"the-challenge-of-multi-operand-adders-in-cnns","repo_url":"https://github.com/MindSpore-paper-code-3/code6/tree/main/AdderNGD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}