{"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/convert-compress-correct-three-steps-toward","title":"Convert, compress, correct: Three steps toward communication-efficient DNN training","arxiv_id":"2203.09044","date":"2022-03-17","proceeding":null,"authors":["Zhong-Jing Chen","Eduin E. Hernandez","Yu-Chih Huang","Stefano Rini"],"abstract":"In this paper, we introduce a novel algorithm, $\\mathsf{CO}_3$, for communication-efficiency distributed Deep Neural Network (DNN) training. $\\mathsf{CO}_3$ is a joint training/communication protocol, which encompasses three processing steps for the network gradients: (i) quantization through floating-point conversion, (ii) lossless compression, and (iii) error correction. These three components are crucial in the implementation of distributed DNN training over rate-constrained links. The interplay of these three steps in processing the DNN gradients is carefully balanced to yield a robust and high-performance scheme. The performance of the proposed scheme is investigated through numerical evaluations over CIFAR-10.","url_abs":"https://arxiv.org/abs/2203.09044v1","url_pdf":"https://arxiv.org/pdf/2203.09044v1.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":"convert-compress-correct-three-steps-toward","repo_url":"https://github.com/chen-zhong-jing/co3_algorithm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}