{"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/halo-hardware-aware-learning-to-optimize","title":"HALO: Hardware-Aware Learning to Optimize","arxiv_id":null,"date":"2020-08-01","proceeding":"ECCV 2020 8","authors":["Chaojian Li","Tianlong Chen","Haoran You","Zhangyang Wang","Yingyan Lin"],"abstract":"There has been an explosive demand for bringing machine learning (ML) powered intelligence into numerous Internet-of-Things (IoT) devices. However, the effectiveness of such intelligent functionality requires in-situ continuous model adaptation for adapting to new data and environments, while the on-device computing and energy resources are usually extremely constrained. Neither traditional hand-crafted (e.g., SGD, Adagrad, and Adam) nor existing meta optimizers are specifically designed to meet those challenges, as the former requires tedious hyper-parameter tuning while the latter are often costly due to the meta algorithms’ own overhead. To this end, we propose hardware-aware learning to optimize (HALO), a practical meta optimizer dedicated to resource-efficient on-device adaptation. Our HALO optimizer features the following highlights: (1) faster adaptation speed (i.e., taking fewer data or iterations to reach a specified accuracy) by introducing a new regularizer to promote empirical generalization; and (2) lower per-iteration complexity, thanks to a stochastic structural sparsity regularizer being enforced. Furthermore, the optimizer itself is designed as a very light-weight RNN and thus incurs negligible overhead. Ablation studies and experiments on five datasets, six optimizees, and two state-of-the-art (SOTA) edge AI devices validate that, while always achieving a better accuracy (↑0.46% - ↑20.28%), HALO can greatly trim down the energy cost (up to ↓60%) in adaptation, quantified using an IoT device or SOTA simulator. Codes and pre-trained models are at https://github.com/RICE-EIC/HALO .","url_abs":"https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/884_ECCV_2020_paper.php","url_pdf":"https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540477.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":"halo-hardware-aware-learning-to-optimize","repo_url":"https://github.com/RICE-EIC/HALO","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}