{"acknowledgement":"We would like to thank Elias Frantar for his valuable assistance and support at the outset of this project, and the anonymous ICML and SNN reviewers for very constructive feedback. EI was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. DA acknowledges generous ERC support, via Starting Grant 805223 ScaleML. ","project":[{"call_identifier":"H2020","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"volume":202,"intvolume":"       202","year":"2023","ec_funded":1,"citation":{"short":"M. Nikdan, T. Pegolotti, E.B. Iofinova, E. Kurtic, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 26215–26227.","ama":"Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In: <i>Proceedings of the 40th International Conference on Machine Learning</i>. Vol 202. ML Research Press; 2023:26215-26227.","ista":"Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. 2023. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 26215–26227.","apa":"Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., &#38; Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In <i>Proceedings of the 40th International Conference on Machine Learning</i> (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.","chicago":"Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In <i>Proceedings of the 40th International Conference on Machine Learning</i>, 202:26215–27. ML Research Press, 2023.","ieee":"M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in <i>Proceedings of the 40th International Conference on Machine Learning</i>, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.","mla":"Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” <i>Proceedings of the 40th International Conference on Machine Learning</i>, vol. 202, ML Research Press, 2023, pp. 26215–27."},"conference":{"start_date":"2023-07-23","name":"ICML: International Conference on Machine Learning","location":"Honolulu, Hawaii, HI, United States","end_date":"2023-07-29"},"language":[{"iso":"eng"}],"_id":"14460","page":"26215-26227","publication":"Proceedings of the 40th International Conference on Machine Learning","author":[{"full_name":"Nikdan, Mahdi","id":"66374281-f394-11eb-9cf6-869147deecc0","first_name":"Mahdi","last_name":"Nikdan"},{"full_name":"Pegolotti, Tommaso","first_name":"Tommaso","last_name":"Pegolotti"},{"full_name":"Iofinova, Eugenia B","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","last_name":"Iofinova","first_name":"Eugenia B","orcid":"0000-0002-7778-3221"},{"last_name":"Kurtic","first_name":"Eldar","full_name":"Kurtic, Eldar","id":"47beb3a5-07b5-11eb-9b87-b108ec578218"},{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"}],"publication_identifier":{"eissn":["2640-3498"]},"external_id":{"arxiv":["2302.04852"]},"status":"public","title":"SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge","quality_controlled":"1","alternative_title":["PMLR"],"date_created":"2023-10-29T23:01:17Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"oa_version":"Preprint","scopus_import":"1","publisher":"ML Research Press","publication_status":"published","date_published":"2023-07-30T00:00:00Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2302.04852","open_access":"1"}],"abstract":[{"lang":"eng","text":"We provide an efficient implementation of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware."}],"department":[{"_id":"DaAl"}],"day":"30","type":"conference","date_updated":"2023-10-31T09:33:51Z","month":"07","article_processing_charge":"No"}