{"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/cpt-efficient-deep-neural-network-training-1","title":"CPT: Efficient Deep Neural Network Training via Cyclic Precision","arxiv_id":"2101.09868","date":"2021-01-25","proceeding":"ICLR 2021 1","authors":["Yonggan Fu","Han Guo","Meng Li","Xin Yang","Yining Ding","Vikas Chandra","Yingyan Celine Lin"],"abstract":"Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs' precision might have a similar effect as the learning rate during DNN training, and advocate dynamic precision along the training trajectory for further boosting the time/energy efficiency of DNN training. Specifically, we propose Cyclic Precision Training (CPT) to cyclically vary the precision between two boundary values which can be identified using a simple precision range test within the first few training epochs. Extensive simulations and ablation studies on five datasets and eleven models demonstrate that CPT's effectiveness is consistent across various models/tasks (including classification and language modeling). Furthermore, through experiments and visualization we show that CPT helps to (1) converge to a wider minima with a lower generalization error and (2) reduce training variance which we believe opens up a new design knob for simultaneously improving the optimization and efficiency of DNN training. Our codes are available at: https://github.com/RICE-EIC/CPT.","url_abs":"https://arxiv.org/abs/2101.09868v4","url_pdf":"https://arxiv.org/pdf/2101.09868v4.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":"cpt-efficient-deep-neural-network-training-1","repo_url":"https://github.com/RICE-EIC/CPT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2101.09868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.09868"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/RICE-EIC/CPT","reach":null}],"summary":{"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"3fed6b12751d0395","entry":"_deflatten_as","repo":"RICE-EIC/CPT","repo_kind":"official","path":"cpt_cifar/modules/quantize.py","file_url":"https://github.com/RICE-EIC/CPT/blob/HEAD/cpt_cifar/modules/quantize.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3fed6b12751d0395"}},{"code_sha256_prefix":"750c588ade0c659c","entry":"calculate_qparams","repo":"RICE-EIC/CPT","repo_kind":"official","path":"cpt_cifar/modules/quantize.py","file_url":"https://github.com/RICE-EIC/CPT/blob/HEAD/cpt_cifar/modules/quantize.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"750c588ade0c659c"}},{"code_sha256_prefix":"3b12a9ff5ac11191","entry":"UniformQuantize","repo":"RICE-EIC/CPT","repo_kind":"official","path":"cpt_cifar/modules/quantize.py","file_url":"https://github.com/RICE-EIC/CPT/blob/HEAD/cpt_cifar/modules/quantize.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3b12a9ff5ac11191"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}